Published Online:April 2026
Product Name:The IUP Journal of Computer Sciences
Product Type:Article
Product Code:IJCS040526
DOI:10.71329/IUPJCS/2026.20.2.53-61
Author Name:Vinaya Kulkarni
Availability:YES
Subject/Domain:Engineering
Download Format:PDF
Pages:53-61
Insulin resistance is an identifiable characteristic of type-2 Diabetes Mellitus (T2DM). By 2030, globally, 643 million people are expected to be diagnosed with diabetes, according to the International Diabetes Federation. Therefore, early prediction is essential to start making lifestyle changes and thus reduce the burden of disease in the future. The goal of this study is to create and test combined machine learning (ML) methods for early prediction of type- 2 diabetes. Compared to individual models, techniques like Random Forest Classifier and Stacking ensembles frequently show better performance. The proposed model is developed using the PIMA Indian Diabetes Dataset, which is frequently used in diabetes prediction research.
One of the most important worldwide public health issues of the twenty-first century is type-2 Diabetes Mellitus (T2DM). Insulin resistance and high blood glucose levels are its symptoms (Khanam & Foo, 2021).