diabetes prediction using machine learning research paper 2022

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J Family Med Prim Care
v.11(11); 2022 Nov
PMC10041290

A survey on diabetes risk prediction using machine learning approaches

Shimoo firdous.

1 Department of Computer Science, Bhagwant University, Ajmer, Rajasthan, India

Gowher A. Wagai

2 Department of Medicine-Associated Hospital GMC, Anantnag, Jammu and Kashmir, India

Kalpana Sharma

Background:.

Diabetes mellitus (DM) is a chronic condition that can lead to a variety of consequences. Diabetes is a condition that is caused by factors such as age, lack of exercise, sedentary lifestyle, family history of diabetes, high blood pressure, depression and stress, poor food, and so on. Diabetics are at a higher risk of developing diseases such as heart disease, nerve damage (diabetic neuropathy), eye problems (diabetic retinopathy), kidney disease (diabetic nephropathy), stroke, and so on. According to the International Diabetes Federation, 382 million people worldwide suffer from diabetes. By 2035, this number will have risen to 592 million. Every day, a large number of people become victims, and many are ignorant whether they have it or not. It primarily affects individuals between the ages of 25 and 74 years. If diabetes is left untreated and undiagnosed, it can lead to a slew of complications. The emergence of machine learning approaches, on the other hand, solves this crucial issue.

Aims and Objectives:

The aim was to study the DM and analyze how machine learning algorithms are used to identify the diabetes mellitus at an early stage, which is one of the most serious metabolic disorders in the world today.

Methods and Materials:

Data was obtained from databases such as Pubmed, IEEE xplore, and INSPEC,and from other secondary sources and primary sources in which methods based on machine learning approaches used in healthcare to predict diabetes at an early stage are reported.

After surveying various research papers, it was found that machine learning classification algorithms like Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) etc shows the best accuracy for predicting diabetes at an early stage.

Conclusion:

Early detection of diabetes is critical for effective therapy. Many people have no idea whether or not they have it. The full assessment of Machine learning approaches for early diabetes prediction and how to apply a variety of supervised and unsupervised machine learning algorithms to the dataset to achieve the best accuracy are addressed in this paper.. Furthermore, the work will be expanded and refined to create a more precise and general predictive model for diabetes risk prediction at an early stage. Different metrics can be used to assess performance and for accurate diabetic diagnosis.

Introduction

Diabetes mellitus is a metabolic disorder defined by abnormally high blood sugar levels due to a lack of insulin secretion or a combination of insulin resistance and insufficient insulin synthesis to compensate.[ 1 ] It is a progressive metabolic ailment that affects all parts of the patient’s life, including physical and mental well-being, and no therapy technique can produce spectacular improvements or stop the disease from progressing.[ 2 ] In the year 2000, India had the greatest number of diabetics in the world (31.7 million), which increased to 62.4 million in 2011 and is anticipated to reach 69.9 million by 2025.[ 3 , 4 ] Rapid urbanization and economic development are to blame for India’s high frequency. Indians are more likely to develop diabetes as a result of their low BMI combined with high upper-body adiposity, high body fat percentage, and high insulin resistance.[ 5 ] Blurred vision, weight loss, fatigue, increased hunger and thirst, confusion, frequent urination, poor healing, frequent infections, and difficulty concentrating are all signs or symptoms of diabetes. “Diabetes means you have too much sugar in your blood. High blood sugar problems start when your body no longer makes enough of a chemical, or hormone, called insulin.”[ 6 ] “Sweet urine” is the direct translation. Normal urine does not contain sugar. There is sugar (or more precisely glucose) in the urine because the amount of glucose in the blood has increased to the point where it spills over into the urine. Because the body is unable to metabolize glucose properly, it accumulates in the bloodstream. As a result, diabetes is a disease in which the body is unable to use glucose properly.

This paper provides many machine learning algorithms used for the early prediction of diabetes. The remainder of the paper is conceived in the following manner: Section (1) is the introduction; section (2) is diabetes and its types; section (3) is machine learning algorithms; section (4) literature survey for prediction of diabetes; and section (5) is the conclusion.

Diabetes and its Types

Diabetes mellitus (DM) is a metabolic disease with a variety of causes. It is characterized by persistent hyperglycemia and alterations in carbohydrate, lipid, and protein metabolism caused by insulin deficiency, insulin action, or both. Diabetes is a chronic disease. Diabetes can injure neurons and blood arteries in the eyes, kidneys, heart, and lower legs if not effectively treated. Problems may emerge if blood glucose levels remain high for an extended period. Gum disease or tooth decay are examples of mouth issues. Diabetic retinopathy is a condition that causes vision loss and, in severe cases, blindness. Heart and blood vessel illnesses include heart attacks, strokes, and peripheral artery disease (cardiovascular diseases or CVD) (insufficient blood supply to the feet and legs). Kidney disease (diabetic nephropathy) is a condition in which the kidneys do not function properly or at all.[ 7 ] The three kinds of diabetes include type 1 diabetes, type 2 diabetes, and gestational diabetes.

Type 1 diabetes (T1D)

The body does not produce enough insulin in type 1 diabetes. Body cells can’t absorb glucose from the bloodstream without insulin, so they have to rely on other sources of energy. An excess of glucose in the blood causes diabetes and its complications. This type of diabetes is also known as insulin-dependent diabetes mellitus (IDDM). Although it affects adolescents and teenagers more frequently, it can affect anyone at any age. It requires a delicate balancing act of insulin injections (and, in some circumstances, oral medicines), exercise, nutrition planning, and lifestyle changes. Frequent urination, unusual thirst, unusual hunger, rapid weight loss, weariness and weakness, nausea, and irritability are some of the symptoms of type 1 diabetes.

Type 2 diabetes (T2D)

Type 2 diabetes can range from primarily insulin resistance to mostly secretory dysfunction with or without insulin resistance. The pancreas produces insulin, but it may not be enough to keep blood glucose levels normal, or the cells may be resistant to the insulin produced. The illness is most prevalent in those over the age of 40, but it is also becoming more prevalent in teenagers and young children. Type 1 diabetes is characterized by drowsiness, dry, itchy skin, unintended weight gain or loss, blurred vision, tingling, numbness, pain in the lower legs, easy weariness, sluggish healing of cuts, or scratches, and frequent infections (e.g., vaginal infections). Food, activity, lifestyle control, and, in some situations, oral medicines or insulin are all necessary for type 2 diabetes.[ 6 ]

Gestational diabetes

Pregnant women who have never had diabetes before but have high blood glucose (sugar) levels during pregnancy are diagnosed with gestational diabetes. It is a temporary condition that affects 2%–4% of all pregnant women and usually disappears after the baby is born. Women who have had gestational diabetes in the past are more likely to develop type 2 diabetes later in life. There is no known etiology for this type of diabetes. The placenta supports the infant’s development; placental hormones help the baby develop, but they also prevent the mother’s insulin from working properly in her body, resulting in insulin resistance. When a mother’s body is unable to create and use all of the insulin required during pregnancy, gestational diabetes develops. The majority of women are unaware of any signs or symptoms of gestational diabetes. Increased thirst and more frequent urination are two symptoms.

Diabetes mellitus is a deadly disorder, if not treated early’ however, early detection can minimize the risk significantly. A range of medical diagnostic procedures is already in use for early diagnosis. Early risk forecasts can be made using machine learning techniques. Recent research has given promising results in terms of forecasting the risk of diabetes mellitus. Machine learning is a field of study in which algorithms are used to teach machines without the need of humans. Without having to explicitly program them, we can train them to do a given job and then use that training to handle similar duties. Accuracy is always a major problem in medical science, and different algorithms might yield varying degrees of accuracy on the same data set. To design a better classifier for better classification, it is vital to figure out which algorithm delivers the greatest results. Machine learning can now be found in almost every industry. Its application in medical science has the potential to improve healthcare dramatically.

Decision trees, random forests, support vector machines, naive Bayes classifiers, and artificial neural networks are examples of machine learning and classification algorithms that work well in risk prediction. Because of the algorithms’ computing and data management skills, this is possible. Measures of classification accuracy can be used to select the best algorithm and determine the best classification accuracy. This statistic, however, is insufficient to properly and efficiently determine the best method. When determining the best conclusion, other variables such as the receiver operating characteristic (ROC) value, F-score, and calculation time should be taken into account. Metrics include classification accuracy, F-score, ROC value, and computation time. Future researchers will be aided by the findings of this study in constructing a baseline strategy for DM classification.

Machine Learning Algorithms

Machine learning (ML) is a rapidly developing field that is being applied in a variety of medical applications. ML models all learn from the past and make predictions based on a data set. Diabetes detection will become much easier and less expensive thanks to recent advances in ML. There are numerous diabetic data sets accessible. As a result, ML is required for medical diagnostics. The goal of this study is to forecast a patient’s probability of developing diabetes. Algorithms for machine learning are employed. There are two types of learning for the study.

1) Supervised Learning
2) Unsupervised Learning.

The goal of a supervised learning algorithm is to predict based on labeled data. In supervised learning, the data is labeled. It simulates what a student might learn from an instructor. Unsupervised learning, on the other hand, does not label the data. It’s more like self-learning based on previous experiences. The goal is to forecast a variable’s value. A set of traits and features are used to represent the data. The outcome of guided learning is predetermined. Decision trees (DT), random forests, linear regression, logistic regression, naive Bayes classifiers, k-nearest neighbors (k-NN), support vector machine (SVM), and artificial neural networks (ANN) are some of the most commonly used techniques.

The data in unsupervised learning is made up of values without labels, and the outcome is not predetermined. Based on self-learning, the model makes predictions. Forecasting, classifying, detecting, segmenting, and categorizing data are the key goals of these models. Machine learning applications include analysis, recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics.

Literature Survey for Prediction of Diabetes using Machine Learning Approaches

Birjais et al .[ 8 ] experimented on PIMA Indian Diabetes (PID) data set. It has 768 instances and 8 attributes and is available in the UCI machine learning repository. They aimed to focus more on diabetes diagnosis, which, according to the World Health Organization (WHO) in 2014, is one of the world’s fastest-growing chronic diseases. Gradient boosting, logistic regression, and naive Bayes classifiers were used to predict whether a person is diabetic or not, with gradient boosting having an accuracy of 86%, logistic regression having a 79% accuracy, and naive Bayes having a 77% accuracy.

Sadhu, A. and Jadli A.[ 9 ] experimented on a diabetes data set taken from the UCI repository. There were 520 occurrences and 16 attributes in all. They attempted to concentrate their efforts on predicting diabetes at an early stage. On the validation set of the employed data set, seven classification techniques were implemented: k-NN, logistic regression, SVM, naive Bayes, decision tree, random forests, and multilayer perceptron. The random forests classifier proved to be the best model for the concerned data set, with an accuracy score of 98%, followed by logistic regression at 93%, SVM at 94%, naive Bayes at 91%, decision tree at 94%, random forests at 98%, and multilayer perceptron at 98%, according to the results of training several machine learning models.

Xue et al .[ 10 ] experimented on the diabetes data set taken from the UCI repository; there were 520 patients and 17 qualities in it. They attempted to concentrate on early detection of diabetes. They trained on the actual data of 520 diabetic patients and probable diabetic patients aged 16–90 using supervised ML techniques such as SVM, naive Bayes classifiers, and LightGBM. The performance of the SVM is the best when comparing classification and recognition accuracy. The naive Bayes classifier is the most widely used classification algorithm, with an accuracy of 93.27%. SVM has the highest accuracy rate of 96.54%. LightGBM has an accuracy of only 88.46%. This demonstrates that SVM is the best classification algorithm for diabetes prediction.

Le et al .[ 11 ] experimented on the early-stage diabetes risk prediction; the data set used in this research was taken from the UCI repository and consisted of 520 patients and 16 variables. They suggested a ML approach for predicting diabetes patients’ early onset. It was a new wrapper-based feature selection method that employed grey wolf optimizer (GWO) and adaptive particle swarm optimization (APSO) to optimize the multilayer perceptron (MLP) and reduce the number of needed input attributes. They also compared the results obtained with this method to those obtained via a variety of traditional machine learning algorithms, including SVM, DT, k-NN, naive Bayes classifier (NBC), random forest classifier (RFC), and logistic regression (LR). LR achieved a 95% accuracy rate. k-NN had a 96% accuracy rate, SVM a 95% accuracy rate, NBC a 93% accuracy rate, DT a 95% accuracy rate, and RFC had a 96% accuracy rate. The suggested methods’ computational findings show that not only are fewer features required but also that higher prediction accuracy may be attained (96% for GWO–MLP and 97% for APSO–MLP). This research has the potential to be applied in clinical practice and used as a tool to assist doctors and physicians.

Julius et al .[ 12 ] used the Waikato Environment for Knowledge Analysis (Weka) application platform to test a data set collected from the UCI repository. There were 520 samples in the data set, each with a collection of 17 attributes. The goal of this study was to use machine learning classification approaches based on observable sample attributes to predict diabetes at an early stage. The k-NN, SVM, functional tree (FT), and RFCs were employed as classifiers. k-NN had the highest accuracy of 98%, followed by SVM at 94%, FT at 93%, and RF at 97%.

Shafi et al .[ 13 ] reported that because diabetes is a serious illness, early detection is always a struggle. This study used machine learning classification methods to develop a model that could solve any problem and that could be used to identify diabetes development early on. The authors of this research made concerted efforts to develop a framework that could accurately predict the likelihood of diabetes in patients. As part of this study, the three ML approach classification algorithms—DT, SVM, and NBC—were studied and assessed on various measures. In the study, the PID data set acquired from the UCI repository was used to save time and produce precise findings. The experimental results suggested that the NBC approach was adequate, with a 74% accuracy, followed by SVM with a 63% accuracy and the DT with a 72% accuracy. In the future, the built framework, as well as the ML classifiers used, could be used to identify or diagnose other diseases. The study, as well as several other ML methodologies, could be extended and improved for diabetes research, and the scientists intended to classify other algorithms with missing data.

Khanam et al .[ 14 ] experimented with diabetes illness prediction. Diabetes is a condition with no known cure; therefore early detection is essential. In this study, data mining, ML techniques, and neural network (NN) methodologies were utilized to predict diabetes. They developed a technique that could accurately predict diabetes. They used data from the UCI repository’s PID data set. The data set included information on 768 patients and their 9 attributes. On the data set, they utilized seven ML methods to predict diabetes: DT, k-NN, RFC, NBC, AB, LR, and SVM. They used the Weka tool to preprocess the data. They discovered that a model combining LR and SVM is effective at predicting diabetes. They created a NN model with two hidden layers and varied epochs and found that the NN with two hidden layers gave 88.6% accuracy. ANN scored 88.57%, LR scored 78.85%, NBC scored 78.28%, and RFC scored 77.34%.

Sisodia et al .[ 15 ] used the PID data set available on the UCI repository. This data set contained 768 patients and 8 attributes. They employed three ML classifications to identify diabetic patients: DT, SVM, and NBC. NBC had the highest accuracy (76.30%) when compared to the other models.

Agarwal et al .[ 16 ] used the PID data set of 738 patients as well in their study. To analyze the effectiveness of this data set for identifying diabetic patients, the authors applied models such as SVM, k-NN, NBC, ID3, C4.5, and CART. The SVM and LDA algorithms were the most accurate, with an accuracy of 88%.

Rathore et al .[ 17 ] employed classification techniques like SVM and DTs to predict diabetes mellitus. The PID data set provided the data for this investigation. PIMA India prioritizes women’s health. The SVM has an accuracy of 82%.

To predict diabetes mellitus, Hassan et al .[ 18 ] employed classification approaches such as the DT, k-NN, and SVM. The SVM outperformed the DT and KNN methods with a maximum accuracy of 90.23%.

Kandhasamy and Balamurali[ 19 ] investigated the prediction accuracy of J48, k-NN, RFC, and SVM on the diabetes data set. Before preprocessing the data, the author discovered that the J48 method had a higher accuracy than others, at 73.82%. After preprocessing, k-NN and RFC demonstrated improved accuracy.

Meng et al .[ 20 ] examined J48, LR, and k-NN algorithms on the diabetes data set. J48 was found to be the most accurate, with a classification accuracy of 78.27%.

Nai-Arun and Moungmai[ 21 ] created a web application based on the prediction accuracy for diabetes prediction. They compared prediction methods such as DTs, NNs, LR, NBC, and RFC, as well as, bagging and boosting. They discovered that RFC performed best in terms of accuracy and ROC score, with an accuracy of 85.558% and an ROC value of 0.912.

Saravananathan and Velmurugan[ 22 ] looked at J48, CART, SVM, and k-NN on a medical data set in their research. They compared them based on accuracy, specificity, sensitivity, precision, and error rate. With a score of 67.15%, they discovered that J48 algorithms were the most accurate, followed by SVM (65.04%), CART (62.28%), and k-NN (53.39%).

Kumari and Chitra[ 23 ] used SVM, RFC, DT, MLP, and LR, as well as four k-fold cross-validations (k = 2,4,5,10) in their research. According to the researchers, MLP with four-fold cross-validation achieves the best accuracy, at 78.7%. They discovered that MLP outscored all other algorithms.

To predict diabetes, Kavakiotis et al .[ 24 ] employed NBC, RFC, k-NN, SVM, DT, and LR methods. The algorithms were applied using a ten-fold cross-validation technique. SVM had the best accuracy of all the approaches, measuring 84%, according to the study.

The work on the classification of “Diabetes Prediction” based on eight attributes was done by Rawat et al .[ 25 ] In this study, five ML algorithms for the analysis and prediction of diabetic patients were described: AdaBoost, LogicBoost, RobustBoost, naive Bayes, and bagging. A group of diabetic PIMA Indians was used to test the proposed strategies. The computed results were found to be quite accurate, with a classification accuracy of 81.77% and 79.69% for the bagging and AdaBoost techniques, respectively. As a result, the proposed DM prediction algorithms were particularly appealing, effective, and efficient.

Using disease classifiers and an actual data set, Nai-Arun and Moungmai[ 21 ] suggested a web application. The data for this component was collected from 30,122 people at Sawanpracharak Regional Hospital’s twenty-six primary care units between 2012 and 2013. To identify a predictive model, thirteen classification models were investigated before the web application was created. These models, except the RFC method, included the DT, NN, LR, NBC, and RFC algorithms, which all used a combination of bagging and boosting techniques. Each model’s accuracy and ROC curves were calculated and compared to others to see how robust they were. According to the findings, RFC won in both accuracy and ROC curve. This could be owing to a wide range of options. Not only were data and input factors chosen at random in the RFC approach, but crucial variables were also taken into account. As a result, the precision values rose. As a result, this algorithm was chosen to represent diabetes risk prediction and was employed in the development of the application.

Perveen et al .[ 26 ] used a data set from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) database to do their research. The study employed the AdaBoost and bagging ensemble techniques using the J48 (C4.5) DT as a base learner and standalone data mining methodology J48 to categorize patients with diabetes mellitus based on diabetes risk indicators. This categorization was done across three separate ordinal adult groups in the CPCSSN. In terms of overall performance, the AdaBoost ensemble method surpassed both bagging and a single J48 DT, according to the findings.

Mujumdar and Vaidehi[ 27 ] presented a diabetes prediction model for better diabetes classification that included a few extrinsic factors that caused diabetes, as well as regular components such as glucose, BMI, age, insulin, and so on. The new data set enhanced classification accuracy when compared to the old data set. Multiple ML approaches were used on the data set, and classification was done with a variety of algorithms, with LR yielding the highest accuracy at 96%. The AdaBoost classifier was found to be the most accurate, with a 98.8% accuracy rate. They used two separate data sets to compare the accuracy of ML techniques. When compared to the existing data set, it was clear that the model improved diabetes prediction accuracy and precision.

Mercaldo et al .[ 28 ] offered a strategy for classifying diabetic patients based on a set of features chosen according to the WHO criteria. Evaluating real-world data using state of the art machine learning algorithms. The model was trained using six alternative classification approaches, with the Hoeffding Tree method scoring 0.770 in precision and 0.775 in recall. They used data from the PIMA Indian community in Phoenix, Arizona, to evaluate the method.

Early detection of diabetes is critical for effective therapy. Many people have no idea whether or not they have it. The full assessment of machine learning approaches for early diabetes prediction and how to apply a variety of supervised and unsupervised machine learning algorithms to the data set to achieve the best accuracy are addressed in this paper. Furthermore, the work will be expanded and refined to create a more precise and general predictive model for diabetes risk prediction at an early stage. Different metrics can be used to assess performance and for accurate diabetic diagnosis.

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Early Prediction of Diabetes Using an Ensemble of Machine Learning Models

Affiliations.

1 Department of Biomedical Engineering (BME), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh.
2 Department of Biomedical Engineering (BME), Military Institute of Science and Technology (MIST), Mirpur Cantonment, Dhaka 1216, Bangladesh.
3 Department of Electrical and Electronic Engineering (EEE), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh.
4 School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia.
5 Electronics and Communication Engineering (ECE) Discipline, Khulna University (KU), Khulna 9208, Bangladesh.
6 Statistics Discipline, Khulna University (KU), Khulna 9208, Bangladesh.
7 Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
PMID: 36231678
PMCID: PMC9566114
DOI: 10.3390/ijerph191912378

Diabetes is one of the most rapidly spreading diseases in the world, resulting in an array of significant complications, including cardiovascular disease, kidney failure, diabetic retinopathy, and neuropathy, among others, which contribute to an increase in morbidity and mortality rate. If diabetes is diagnosed at an early stage, its severity and underlying risk factors can be significantly reduced. However, there is a shortage of labeled data and the occurrence of outliers or data missingness in clinical datasets that are reliable and effective for diabetes prediction, making it a challenging endeavor. Therefore, we introduce a newly labeled diabetes dataset from a South Asian nation (Bangladesh). In addition, we suggest an automated classification pipeline that includes a weighted ensemble of machine learning (ML) classifiers: Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), XGBoost (XGB), and LightGBM (LGB). Grid search hyperparameter optimization is employed to tune the critical hyperparameters of these ML models. Furthermore, missing value imputation, feature selection, and K-fold cross-validation are included in the framework design. A statistical analysis of variance (ANOVA) test reveals that the performance of diabetes prediction significantly improves when the proposed weighted ensemble (DT + RF + XGB + LGB) is executed with the introduced preprocessing, with the highest accuracy of 0.735 and an area under the ROC curve (AUC) of 0.832. In conjunction with the suggested ensemble model, our statistical imputation and RF-based feature selection techniques produced the best results for early diabetes prediction. Moreover, the presented new dataset will contribute to developing and implementing robust ML models for diabetes prediction utilizing population-level data.

Keywords: South Asian diabetes dataset; artificial intelligence; diabetes prediction; ensemble ML classifier; filling missing value; outlier rejection.

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The authors announce that they have no known competing financial interest or personal relationships that could have appeared to affect the outcome documented in this article.

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MINI REVIEW article

Prediction of type-2 diabetes mellitus disease using machine learning classifiers and techniques.

$\nB. Shamreen Ahamed$

Department of Computer Science and Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Chennai, India

The technological advancements in today's healthcare sector have given rise to many innovations for disease prediction. Diabetes mellitus is one of the diseases that has been growing rapidly among people of different age groups; there are various reasons and causes involved. All these reasons are considered as different attributes for this study. To predict type-2 diabetes mellitus disease, various machine learning algorithms can be used. The objective of using the algorithm is to construct a predictive model to critically predict whether a person is affected by diabetes. The classifiers taken are logistic regression, XGBoost, gradient boosting, decision trees, ExtraTrees, random forest, and light gradient boosting machine (LGBM). The dataset used is PIMA Indian Dataset sourced from UC Irvine Repository. The performance of these algorithms is compared in reference to the accuracy obtained. The results obtained from these classifiers show that the LGBM classifier has the highest accuracy of 95.20% in comparison with the other algorithms.

Introduction

Diabetes mellitus (DM) is considered as a chronic disease that has been affecting people of all age groups. The exact cause of the disease is still unknown. However, some of the factors or causes include age, family history, other relative diseases, pregnancy, fluctuating glucose levels, blood pressure, etc. ( Dash et al., 2019 ). Diabetes is a disease that can be controlled under medication; however, a complete cure through medicines is not possible as of today. Diabetes can belong to one of the four broad categories, such as type-1, type-2, gestational diabetes, or prediabetes ( Nibareke and Laassiri, 2020 ). There are some sub-types classified under these four categories as well. “Type-1 diabetes” is also known as “insulin-dependent diabetes,” which occurs when the insulin release cell is damaged and unable to produce insulin ( Martinsson et al., 2020 ). In “type-2” diabetes, adequate amount of insulin is not produced in the body ( Wang et al., 2015 ). This commonly happens at an average above age of 40 years. The “gestational diabetes (GDM)” occurs mostly during pregnancy. The last one among the main four categories, “prediabetes,” occurs when the blood sugar level is higher than normal but not as high as type-2 diabetes ( Mujumdar and Vaidehi, 2019 ).

In the recent years, many researchers are using the concept of machine learning to predict the DM disease. Some of the commonly used algorithms include logistic regression (LR), XGBoost (XGB), gradient boosting (GB), decision trees (DTs), ExtraTrees, random forest (RF), and light gradient boosting machine (LGBM). Each classifier has its own advantages over the other classifiers ( Prabha et al., 2021 ). However, the classifier that gives the highest accuracy is determined in implementation.

This study is divided into different sections as follows: Section Related Works represents the related works in DM. Section Theoretical Concepts of the Classifiers determines the theoretical concepts of the various algorithms used. Section Results and Discussion determines the architecture and implementation of the classifiers. Section Conclusion and Future Work explains the conclusions and future works of the study.

Related Works

The following researchers have used the concept of machine learning for predicting DM disease.

Khaleel and Al-Bakry (2021) have created a model to detect whether a person is affected with DM disease. The concept of machine learning (ML) is used for the detection procedures. The PIMA dataset is used for the study. The algorithms used are LR, Naive bayes (NB), and K-nearest neighbour (KNN). The accuracy obtained are 94, 79, and 69% from these algorithms. The measures such as precision, recall, and F-measure are taken into consideration and LR is considered to produce the highest accuracy.

Ahmed et al. (2021) have used ML algorithms, namely, DT, KNN, NB, RF, GB, LR, and support vector machine (SVM) for predicting DM. Preprocessing techniques, such as label–encoding–normalization, are used to increase the accuracy. Two different datasets are used. One dataset provides the highest accuracy for SVM with 80.26% and for the second dataset, the highest accuracy is given by DT and RF with 96.81%.

Maniruzzaman et al. (2018) have used the ML technique based on risk-stratification is developed, optimized and evaluated. Features are optimized using six feature selection techniques. Then PIMA Indian diabetes dataset (PIDD) is used. The 10 different classifiers are used. Both RF selection and RF classification techniques yield an accuracy of 92.26%.

Kumari et al. (2021) have used two datasets including PIDD and breast cancer dataset, which were taken from the UC Irvine (UCI) Repository. Three ML classifiers are used for prediction. They are RF, LR, and Naive Bayes. The accuracy obtained is the highest for both datasets with a percentage of 79.08% for PIMA data and 97.27% for breast cancer data using soft voting classifier.

Tigga and Garg (2020) have developed a prediction model for DM disease. A dataset was collected for the study consisting of 952 instances and 18 attributes. The PIMA dataset was also used. The machine learning classifiers used are RF, LR, KNN, SVM, NB, and DT. The accuracy obtained was the highest for RF with a percentage of 94.10% for collected data and 75% for PIMA dataset.

Diwani and Sam (2014) have developed a prediction model using 10-fold-cross-validation on the training and testing data. The Waikato environment for knowledge analysis tool has been used along with Naive Bayes and DTs algorithm. The accuracy obtained is the highest for Naive Bayes with 76.30%.

Butt et al. (2021) have proposed a machine learning based approach for early-stage identification, classification, and prediction of diabetes disease. The PIMA Indian dataset has been used. The classifiers used are RF, multilayer perceptron (MLP) and LR. The accuracy obtained is highest for MLP with 87.26%.

Theoretical Concepts of the Classifiers

The various classifiers that are used is explained in the following sub-sections.

Logistic Regression

It is a statistics-based model that uses logical function to develop a binary-dependent variable. The relationship between dependent and independent variables is estimated based on probabilities ( Diwani and Sam, 2014 ). The dependent variable is categorical in this method. Mathematically it is expressed as follows ( Kaur and Chhabra, 2014 ):

The probability that Y = 1 given X which is given as “ theta ”

The XGBoost

It is the implementation of gradient boosted DTs that are created sequentially. An important feature is its weights. Each individual variable is assigned a particular weight that are given to the DTs to obtain the results ( Butt et al., 2021 ). The prediction scores of each individual DT is given by

where the number of trees is denoted by k , the functional space is given as f , and the possible set available is given as F ( Patil et al., 2019 ).

Gradient Boosting

Many weak learners are combined into a predictive model typically in the form of DTs ( Sehly and Mezher, 2020 ). It is mainly used when we want to decrease the bias error. A gradient-descent technique is chosen to obtain values of the coefficients ( Posonia et al., 2020 ).

The loss function used is ( y 1 − y 1′) 2 . y 1 is the actual value and y 1′ is the final predicted value by this model. So y 1′ is replaced with G n ( X ), which represents the actual target ( Ke et al., 2017 ). It is mathematically expressed as follows:

Decision Trees

It is a supervised-learning algorithm ( Islam et al., 2020 ). It works with categorical and continuous input and output variables. It is used to represent whether it belongs to classification or regression procedures ( Chen and Guestrin, 2016 ). The types of DTs are as follows: ID3, ID 4.5, CART, and CHAID. The measures used on DT are as follows: Entropy, Gini index, and standard deviation ( Khanam and Foo, 2021 ). It is mathematically calculated as follows ( Ambigavathi and Sridharan, 2018 ):

Extra Trees

Extra trees (ETs) are also called as “extremely randomized trees classifier.” It is a type of “ensemble learning technique” which combines many decorrelated DTs to result as a single tree classification ( Chen et al., 2017 ). It differs from RF in a way in which DTs are built. The entropy is calculated as follows:

where the number of unique class labels is given as c 1, the proportion of rows with output label is given as p i 1 ( Sisodia and Sisodia, 2018 ).

Then the “information gain” is calculated using the following formula ( Ke et al., 2017 ):

Random Forest

The RF combines the output of multiple DT to reach a single result. The DT is taken as a base and row sampling as well as column sampling. The number of base learners is increased and the variance is decreased or vice versa . For cross-validation, K can be used. It is considered as an important bagging method ( Mamuda and Sathasivam, 2017 ).

Random Forest =DT (base learner) + bagging (Row sampling with replacement) + feature

bagging (column sampling) + aggregation

(mean/median, majority vote)

Light Gradient Boosting Machine

The performance of LGBM is considered to be high-performance and is represented as “GB framework” based on DT algorithm ( Ahamed and Arya, 2021 ). It is majorly used for classifying and ranking. It splits the tree leaf-wise with best-fit. It can be measured using the data improvement technique and can be given by calculating the variance after segregating ( Zhu et al., 2020 ). It can be represented as follows:

System Architecture

The data needed for the study are initially collected and stored in the database. The dataset PIMA is taken from UCI Repository for execution. The dataset is then pre-processed using different exploratory data analysis techniques. The dataset is divided into “training data” and “testing data.” The various algorithms mentioned are then compared and the best working algorithm producing the highest accuracy is taken as the best predictive model for predicting DM disease. The architectural structure depicted in Figure 1 .

Figure 1 . Architectural design.

Results and Discussion

The results and accuracy percentage calculated are given in the form of a table ( Table 1 ).

Table 1 . Accuracy percentage.

The algorithms considered are LR, XGB, GB, DT, ET, RF, and LGBM. The accuracy obtained is the highest for LGBM with 95.2%.

Conclusion and Future Work

These discussions here were considered and we identified that “LGBM algorithm” worked best for the dataset taken by producing an accuracy that was higher in comparisons with the other algorithms. However, in future, different dataset can be taken and compared with the different classifiers to classify which algorithm can produce the best result. Also, the parameters using in LGBM can be further finetuned and an advanced LGBM algorithm can be used and the prediction accuracy percentage can be increased.

Author Contributions

BA and MA: material preparation, data collection, analysis, resources, and writing—review and editing. BA: first draft of the manuscript and investigation. MA: conceptualization, supervision, and visualization. AN: coding and idea of research. All authors contributed to the study conception and design, involved in the idea for the article, performed the literature search, data analysis, drafted, and critically revised the work.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: prediction, machine learning, classifiers, accuracy, comparison

Citation: Ahamed BS, Arya MS and Nancy V AO (2022) Prediction of Type-2 Diabetes Mellitus Disease Using Machine Learning Classifiers and Techniques. Front. Comput. Sci. 4:835242. doi: 10.3389/fcomp.2022.835242

Received: 14 December 2021; Accepted: 08 April 2022; Published: 10 May 2022.

Reviewed by:

Copyright © 2022 Ahamed, Arya and Nancy V. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: B. Shamreen Ahamed, shamu1502@gmail.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Research on Diabetes Prediction Method Based on Machine Learning

Jingyu Xue 1 , Fanchao Min 1 and Fengying Ma 1

Published under licence by IOP Publishing Ltd Journal of Physics: Conference Series , Volume 1684 , The 2020 International Seminar on Artificial Intelligence, Networking and Information Technology 18-20 September 2020, Shanghai, China Citation Jingyu Xue et al 2020 J. Phys.: Conf. Ser. 1684 012062 DOI 10.1088/1742-6596/1684/1/012062

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Diabetes mellitus (DM) is a metabolic disease characterized by high blood sugar. The main clinical types are type 1 diabetes and type 2 diabetes. Now, the proportion of young people suffering from type 1 diabetes has increased significantly. Type 1 diabetes is chronic when it occurs in childhood and adolescence, and has a long incubation period. The early symptoms of the onset are not obvious, which may lead to failure to detect in time and delay treatment. Long-term high blood sugar can cause chronic damage and dysfunction of various tissues, especially eyes, kidneys, heart, blood vessels and nerves. Therefore, the early prediction of diabetes is particularly important. In this paper, we use supervised machine-learning algorithms like Support Vector Machine (SVM), Naive Bayes classifier and LightGBM to train on the actual data of 520 diabetic patients and potential diabetic patients aged 16 to 90. Through comparative analysis of classification and recognition accuracy, the performance of support vector machine is the best.

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Analysis and Prediction of Diabetes Using Machine Learning

International Journal of Emerging Technology and Innovative Engineering, Volume 5, Issue 4, April 2019

9 Pages Posted: 23 Apr 2019

Sri Krishna College of Technology, Students

S. Subashree

Date Written: April 2, 2019

Healthcare industry contains very large and sensitive data and needs to be handled very carefully. Diabetes Mellitus is one of the growing extremely fatal diseases all over the world. Medical professionals want a reliable prediction system to diagnose Diabetes. Different machine learning techniques are useful for examining the data from diverse perspectives and synopsizing it into valuable information. The accessibility and availability of huge amounts of data will be able to provide us useful knowledge if certain data mining techniques are applied to it. The main goal is to determine new patterns and then to interpret these patterns to deliver significant and useful information for the users. Diabetes contributes to heart disease, kidney disease, nerve damage, and blindness. Mining the diabetes data in an efficient way is a crucial concern. The data mining techniques and methods will be discovered to find the appropriate approaches and techniques for efficient classification of Diabetes dataset and in extracting valuable patterns. In this study, medical bioinformatics analyses have been accomplished to predict diabetes. The WEKA software was employed as a mining tool for diagnosing diabetes. The Pima Indian diabetes database was acquired from UCI repository used for analysis. The dataset was studied and analyzed to build an effective model that predicts and diagnoses diabetes disease. In this study, we aim to apply the bootstrapping resampling technique to enhance the accuracy and then applying Naïve Bayes, Decision Trees and (KNN) and compare their performance.

Keywords: Healthcare, Diabetes, Classification, K-nearest neighbors, Decision Trees, Naive Bayes

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Development and validation of a machine learning-based readmission risk prediction model for non-ST elevation myocardial infarction patients after percutaneous coronary intervention

Yanxu Liu , Linqin Du , +14 authors Rongchuan Yue
Published in Scientific Reports 11 June 2024

27 References

Leveraging machine learning techniques to forecast patient prognosis after percutaneous coronary intervention., machine learning enhances the performance of short and long-term mortality prediction model in non-st-segment elevation myocardial infarction, risk prediction for 30-day heart failure-specific readmission or death after discharge: data from the korean acute heart failure (korahf) registry., machine learning prediction models for in-hospital mortality after transcatheter aortic valve replacement., prognostic impact of b-type natriuretic peptide on long-term clinical outcomes in patients with non-st-segment elevation acute myocardial infarction without creatine kinase elevation., prognostic impact of admission high-sensitivity c-reactive protein in acute myocardial infarction patients with and without diabetes mellitus, thirty-day readmissions after chronic total occlusion percutaneous coronary intervention in the united states: insights from the nationwide readmissions database., predictive model for heart failure readmission using nationwide readmissions database, prediction of unplanned 30-day readmission for icu patients with heart failure, length of stay and risk of very early readmission in acute heart failure., related papers.

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IDBNWP: Improved deep belief network for workload prediction: Hybrid optimization for load balancing in cloud system

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A. Ajil 1 , 2 &
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The achievement of cloud environment is determined by the efficiency of its load balancing with proper allocation of resources. The proactive forecasting of future workload, accompanied by the allocation of resources, has emerged as a primary method for addressing other inbuilt problems, such as the underneath or over utilization of physical machines, resource wastage, VM migration, Quality-of-Services (QoS) violations, load balancing, and so on. In this paper, we have introduced a novel workload prediction and load balancing approach which includes two major phases like workload prediction with deep learning and optimal load balancing. In the initial workload prediction stage, we have proposed an Improved Deep Belief Network (IDBN), which efficiently predict the load as under load, overload or equally balanced. Afterwards, the load gets balanced by the utilization of the hybrid optimization named Bald Eagle Assisted Butterfly Optimization Algorithm (BEABOA), which consider the constraints like makespan (70), communication cost (4000), response time (1.1), turnaround time (2), migration cost (0.1) during the process of optimal load balancing. Also, the outcomes demonstrate that this proposed workload prediction and load balancing approach can offer superior outcomes.

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Ajil, A., Kumar, E.S. IDBNWP: Improved deep belief network for workload prediction: Hybrid optimization for load balancing in cloud system. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19495-z

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Testing machine learning models to predict postoperative ileus after colorectal surgery.

1. Introduction

2. materials and methods, 2.1. study design and participants, 2.2. data acquisition and variable selection, 2.3. statistical analysis, 2.4. model training and validation, model descriptions, 2.5. model performance, 3.1. baseline characteristics, 3.2. comorbidities of importance, 3.3. ml model performance, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest, abbreviations.

Ada boosting classifier	adaptive boosting classifier
ASA physical status	American Society of Anesthesiologists Physical Status
AUC ROC curve	area under the curve receiver operating characteristic curve
BMI	body mass index
CCI	Charlson Comorbidity Index
CVA	cerebral vascular accident
ECI	Elixhauser Comorbidity Index
EHR	electronic health record
EDA	exploratory data analysis
XG boosting classifier	extreme gradient boosting classifier
IQR	inter-quantile range
kNN	k-nearest neighbors imputation
LOS	length of stay
ML	machine learning
NSQIP	National Surgical Quality Improvement Program index
POEM	perioperative evaluation and management
POI	postoperative ileus
PPV	positive predictive values
SMOTE	synthetic minority oversampling technique
UHC	University Health System Consortium

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Click here to enlarge figure

Data Science Stage	Sub-Stage	Description	Tools/Metrics
Data Acquisition	Source Import	Load data from CSV files, databases, etc.	File paths, data size
Data Acquisition	Cleaning and Preprocessing	Check for missing values, inconsistencies, duplicates. Format data types.	Imputation methods, error checking tools
Exploratory Data Analysis (EDA)	Feature Distribution	Analyze data distribution for each feature using histograms, boxplots.	Visualizations, skewness measures
	Feature Relationships	Identify relationships between features and target variable using scatter plots, correlation matrices.	Correlation coefficients, feature importance scores
	Outlier and Bias Detection	Check for outliers and potential biases using boxplots, statistical tests.	Outlier detection algorithms, bias analysis tools
Imbalanced Data Handling	Class Imbalance Assessment	Calculate class imbalance ratio, visualize class distribution using pie charts.	Class imbalance ratio, visualization tools
Imbalanced Data Handling	Mitigation Strategy Decision	Choose appropriate strategy: SMOTE, undersampling, oversampling, none.	Imbalance severity, data type, problem type
Data Oversampling (Optional)	SMOTE Application	Apply SMOTE or other oversampling techniques to increase minority class.	SMOTE algorithms, minority class size increase
Data Oversampling (Optional)	Oversampling Control	Ensure oversampling does not introduce overfitting or class overlap.	Cross-validation, visualization
Data Undersampling (Optional)	Undersampling Techniques	Apply undersampling techniques to reduce majority class.	Random undersampling, stratified undersampling
Data Undersampling (Optional)	Undersampling Control	Ensure undersampling does not introduce bias or loss of information.	Class balance metrics, cross-validation
Model Selection and Training	Feature Engineering (Optional)	Create new features based on existing ones (ratios, transformations).	Feature engineering algorithms, interpretability measures
	Model Selection	Choose suitable ML algorithms based on data type, problem type, and interpretability needs.	Logistic Regression, Random Forest, Decision Trees, Gradient Boosting, Extreme Gradient Boosting
	Model Training and Regularization	Split data into training, validation, and test sets. Train models with cross-validation and regularization (L1, L2).	Train/validation/test ratios, regularization parameters
Model Evaluation and Testing	Model Validation	Evaluate model performance on validation set using accuracy, precision, recall, F1-score, AUC-ROC (for imbalanced data).	Validation set metrics, model comparison tools
	Best Model Selection	Compare performance across models and select the best one.	Validation metrics comparison, statistical tests
	Model Testing	Evaluate final model on unseen test set to assess real-world performance.	Test set metrics, model generalization analysis
	Error Analysis	Analyze model errors and identify potential limitations.	Error analysis tools, visualization
Production	Interpretation and Deployment	Interpret model results and explain predictions. Deploy model and monitor performance.	Explainable AI tools, model monitoring systems

Variable of Importance	No Ileus (n = 296)	SD/Range	Ileus (n = 20)	SD/Range	Chi-Square	p-Value
Gender					2.603	0.107
Male	153		14 (70%)
Female	143		6 (30%)
Age (mean/SD)	58	+/−12.33	62	+/−10.05		0.055
BMI (median/range)	21.8	17.31–56.10	30.5	20.94–41.53		0.00
NISQP (median/range)	33.6	13.01–46.12	56.2	45.1–78.4		0.00
Length of Stay (Days) (median/range)	3.74	1–20	11.64	6–25		0.00
Cost of Care (Ratio)	1.0	+/−0.36	1.77	+/−0.34
Co-Morbidity
Kidney Disease	50		5 (25%)		0.629	0.428
Anemia	77		5 (25%)		0.033	0.855
Arrhythmia	41		4 (20%)		0.458	0.498
Rheumatoid Arthritis	32		4 (20%)		1.613	0.204
Surgical Approach
Coloanal Anastomosis	9		4 (20%)		0.703	0.402
Extended Right Hemicolectomy	14		2 (10%)		0.259	0.611
Left Hemicolectomy	21		1 (5%)		0.091	0.763
Low Anterior Resection	161		3 (15%)		0.085	0.771
Right Hemicolectomy	64		3 (15%)		0.091	0.763
Sigmoid Colectomy	13		1 (5%)		0.154	0.695
Subtotal Colectomy (Ileosigmoid)	1		0		0.000	0.996
Total Colectomy, Ileorectal	1		1 (5%)		1.047	0.306
Transverse Colectomy	1		0		0.000	0.996
Ultra Low Anterior Resection	11		1 (5%)		0.167	0.683
Surgery Type					3.848	0.050
Minimally Invasive Surgery (MIS)	248 (95%)		13 (5%)
Open Approach	48 (87.3%)		7 (12.7%)

Co-Morbidity	Sample Size	Frequency	% of Sample
HTN	316	178	56.3%
CAD	316	62	19.6%
Past MI	316	17	5.4%
CHF	316	25	7.9%
CABG Stent	316	20	6.3%
Arrhythmia	316	41	13.0%
AICD	316	1	0.3%
Pacemaker	316	51	16.1%
Valvular	316	18	5.7%
PVD	316	7	2.2%
Anemia	316	77	24.4%
Diabetes	316	63	19.9%
Hypothyroidism	316	56	17.7%
Electrolyte Disturbance	316	308	97.5%
Asthma	316	60	19.0%
COPD	316	30	9.5%
OSA	316	48	15.2%
CVA	316	65	20.6%
TIA	316	6	1.9%
Seizures	316	7	2.2%
Neuromuscular Disease	316	0	0.0%
Hepatitis	316	26	8.2%
Cirrhosis	316	13	4.1%
AIDS_HIV	316	3	0.9%
Dyslipidemia	316	118	37.3%
Kidney Disease	316	50	15.8%
RA	316	32	10.1%
Depression	316	39	12.3%
Dementia	316	39	12.3%

	AdaBoost Tuned with Grid Search	AdaBoost Tuned with Random Search	XGboost Tuned with Grid Search	XGboost Tuned with Random Search
Accuracy	0.942	0.942	0.852	0.852
Recall	0.083	0.083	0.833	0.833
Precision	1.000	1.000	0.278	0.278
F1	0.154	0.154	0.417	0.417

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Brydges, G.; Chang, G.J.; Gan, T.J.; Konishi, T.; Gottumukkala, V.; Uppal, A. Testing Machine Learning Models to Predict Postoperative Ileus after Colorectal Surgery. Curr. Oncol. 2024 , 31 , 3563-3578. https://doi.org/10.3390/curroncol31060262

Brydges G, Chang GJ, Gan TJ, Konishi T, Gottumukkala V, Uppal A. Testing Machine Learning Models to Predict Postoperative Ileus after Colorectal Surgery. Current Oncology . 2024; 31(6):3563-3578. https://doi.org/10.3390/curroncol31060262

Brydges, Garry, George J. Chang, Tong J. Gan, Tsuyoshi Konishi, Vijaya Gottumukkala, and Abhineet Uppal. 2024. "Testing Machine Learning Models to Predict Postoperative Ileus after Colorectal Surgery" Current Oncology 31, no. 6: 3563-3578. https://doi.org/10.3390/curroncol31060262

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Feature papers represent the most advanced research with significant potential for high impact in the field. ... Paul, D.; Ghosh, P. Analysing feature importances for diabetes prediction using machine learning. In Proceedings of the 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver ...
Diabetes Mellitus Disease Prediction Using Machine Learning Classifiers
Research Article Diabetes Mellitus Disease Prediction Using Machine Learning Classifiers with Oversampling and Feature Augmentation B. Shamreen Ahamed ,1 Meenakshi S. Arya,2 and Auxilia Osvin V. Nancy1 1College of Engineering and Technology, SRM Institute of Science and Technology, Vadapalani Campus, No. 1, Jawaharlal Nehru Road, Vadapalani, Chennai, Tamil Nadu, India
Diabetes Prediction Using Machine Learning Algorithms
Diabetes patients with poor control is a kind of diabetes that affects the body's metabolism characterized by an abnormal rise in blood sugar levels due to insulin deficiency, tissue insulin sensitivity, or both. In the sphere of medicine, the diabetes system is quite beneficial. Diabetes prevention is critical, and utmost caution should be exercised to avoid it. Diabetes has now become a ...
Prediction of Diabetes using Machine Learning
Download Citation | On Oct 13, 2022, C.S. Manikandababu and others published Prediction of Diabetes using Machine Learning | Find, read and cite all the research you need on ResearchGate
Diabetes Prediction Using Machine Learning
Prediction of Diabetes and Symptoms of Covid-19 Using Machine Learning Classifiers. Conference Paper. May 2022. Steffy T. Baby. Basil Xavier Simon. G. Jaspher W. Kathrine. Request PDF | On Dec 27 ...
Diabetes prediction using supervised machine learning
Supervised Learning is a machine learning technique that is used for machine learning with labeled datasets in order to identify input labels in order to make predictions and classifications [1]. 1.1. Research Problem In this study, the research problem is because there were 463 million diabetics in 2019.
A survey on diabetes risk prediction using machine learning approaches
The goal of this study was to use machine learning classification approaches based on observable sample attributes to predict diabetes at an early stage. The k-NN, SVM, functional tree (FT), and RFCs were employed as classifiers. k-NN had the highest accuracy of 98%, followed by SVM at 94%, FT at 93%, and RF at 97%.
Early Prediction of Diabetes Using an Ensemble of Machine Learning
In conjunction with the suggested ensemble model, our statistical imputation and RF-based feature selection techniques produced the best results for early diabetes prediction. Moreover, the presented new dataset will contribute to developing and implementing robust ML models for diabetes prediction utilizing population-level data. Keywords ...
Prediction of Type-2 Diabetes Mellitus Disease Using Machine Learning
The technological advancements in today's healthcare sector have given rise to many innovations for disease prediction. Diabetes mellitus is one of the diseases that has been growing rapidly among people of different age groups; there are various reasons and causes involved. All these reasons are considered as different attributes for this study. To predict type-2 diabetes mellitus disease ...
Diabetes prediction using machine learning and explainable AI techniques
In this paper, an automatic diabetes prediction system has been developed using a private dataset of female patients in Bangladesh and various machine learning techniques. The authors used the Pima Indian diabetes dataset and collected addi-tional samples from 203 individuals from a local textile factory in Bangladesh.
PDF Diabetes Prediction Using Machine Learning Algorithms
Salliah Shafia and Prof. Gufran Ahmad Ansari designed a model for Early Prediction of Diabetes Disease & Classification of Algorithms Using Machine Learning Approach. this research uses the WEKA tools to predict diabetes in patients from Pima India Diabetes Data Set consists of 7 attributes and 767 entries and in this paper,
Research on Diabetes Prediction Method Based on Machine Learning
Long-term high blood sugar can cause chronic damage and dysfunction of various tissues, especially eyes, kidneys, heart, blood vessels and nerves. Therefore, the early prediction of diabetes is particularly important. In this paper, we use supervised machine-learning algorithms like Support Vector Machine (SVM), Naive Bayes classifier and ...
Analysis and Prediction of Diabetes Using Machine Learning
Diabetes contributes to heart disease, kidney disease, nerve damage, and blindness. Mining the diabetes data in an efficient way is a crucial concern. The data mining techniques and methods will be discovered to find the appropriate approaches and techniques for efficient classification of Diabetes dataset and in extracting valuable patterns.
Proteomic prediction of diverse incident diseases: a machine learning
We show the value of broad-capture proteomic biomarker discovery studies across multiple diseases of diverse causes, pointing to those that might benefit the most from proteomic approaches, and the potential to derive common sparse biomarker panels for prediction of multiple diseases at once. This framework could enable follow-up studies to explore the generalisability of proteomic models and ...
Diabetes Prediction Using Machine Learning Algorithm
Diabetes mellitus (DM), commonly known as diabetes, is a group of diseases that are defined by chronic high blood. glucose levels due to abnormalities in insulin secretion, insulin action, or both ...
A Novel Study on Machine Learning Algorithm‐Based Cardiovascular
Hybrid machine learning models have been applied to predict heart diseases as well as perform optimum classification methods for prediction. Hybrid models give a better optimum output depending on the machine learning method implemented for the execution . Similarly, random forest, decision trees, and hybrid algorithms have been used to predict ...
Development and validation of a machine learning-based readmission risk
The findings indicated that the LR model exhibited the most optimal performance in terms of AUC, accuracy, sensitivity, and specificity for the occurrence of readmissions after direct PCI in NSTEMI patients. To investigate the factors that influence readmissions in patients with acute non-ST elevation myocardial infarction (NSTEMI) after percutaneous coronary intervention (PCI) by using ...
Comparative Analysis of Supervised Machine Learning Algorithms for
Given the nature of the disease, it is needed to mitigate the effects of spread by resorting to technological advancements for diagnosis of the disorder using machine learning algorithms. In this paper, three supervised machine learning algorithms; Decision Tree, Naïve Bayes, and Logistic Regression have been utilized for the prediction of the ...
Diabetes Prediction Using Machine Learning Algorithms
A Comparative Analysis of Diabetes Prediction Models using Machine Learning Algorithms. Conference Paper. Mar 2022. S. Sarathambekai. Vairam Th. Sathyaseelan Krishnaraj. Jawhar MG.
IDBNWP: Improved deep belief network for workload prediction ...
In 2021, Sounak Banerjee et al suggested a multi-step-ahead task forecasting approach using machine learning techniques and allocating the resources in accordance with this prediction in a way that allows the resources to be used more effectively and, as a result, lowers the data center's overall energy consumption. Relying on Bitbrains' actual ...
Testing Machine Learning Models to Predict Postoperative Ileus after
Background: Postoperative ileus (POI) is a common complication after colorectal surgery, leading to increased hospital stay and costs. This study aimed to explore patient comorbidities that contribute to the development of POI in the colorectal surgical population and compare machine learning (ML) model accuracy to existing risk instruments. Study Design: In a retrospective study, data were ...
Machine Learning Based Assistance to Healthcare Professionals in
Abstract We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for ...

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A survey on diabetes risk prediction using machine learning approaches

Gowher A. Wagai

Kalpana Sharma

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Diabetes and its Types

Type 1 diabetes (T1D)

Type 2 diabetes (T2D)

Gestational diabetes

Machine Learning Algorithms

Literature Survey for Prediction of Diabetes using Machine Learning Approaches

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Early Prediction of Diabetes Using an Ensemble of Machine Learning Models

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Research on Diabetes Prediction Method Based on Machine Learning

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