Early Prediction of Diabetes Using an Ensemble of Machine Learning Models

Aishwariya Dutta, Md. Kamrul Hasan, Mohiuddin Ahmad, Md. Abdul Awal (), Md. Akhtarul Islam, Mehedi Masud and Hossam Meshref
Additional contact information
Aishwariya Dutta: Department of Biomedical Engineering (BME), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh
Md. Kamrul Hasan: Department of Electrical and Electronic Engineering (EEE), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh
Mohiuddin Ahmad: Department of Electrical and Electronic Engineering (EEE), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh
Md. Abdul Awal: School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia
Md. Akhtarul Islam: Statistics Discipline, Khulna University (KU), Khulna 9208, Bangladesh
Mehedi Masud: Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Hossam Meshref: Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia

IJERPH, 2022, vol. 19, issue 19, 1-25

Abstract: 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: artificial intelligence; diabetes prediction; ensemble ML classifier; filling missing value; outlier rejection; South Asian diabetes dataset (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (3)

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