International Journal on Science and Technology

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COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS AND MAJORITY VOTING ENSEMBLE FOR DIABETES PREDICTION

Author(s) Dr. Sangita Baruah, Dr. Chandan Jyoti Kumar, Ms. Bipasha Bhattacharjee
Country India
Abstract Diabetes is a rapidly growing chronic health condition affecting millions worldwide. Early detection is critical to managing and mitigating the long-term complications associated with the disease. In this study, we perform a comparative analysis of machine learning models for diabetes detection using the Pima Indians Diabetes Dataset. Model performance was evaluated across three experimental phases: (1) using all available features, (2) using selected features obtained through the Recursive Feature Elimination (RFE) algorithm, and (3) separating attributes into clinical and non-clinical groups to assess their individual predictive potential. To enhance robustness, a majority voting ensemble was implemented by combining Linear Discriminant Analysis, Decision Tree, and Random Forest classifiers. The results show that the full feature set consistently achieved better performance compared to RFE-selected subsets, while clinical features outperformed non-clinical features but did not surpass the complete feature set. Majority voting improved stability in most scenarios, with the best overall performance obtained using all features. This study highlights the importance of dataset-specific evaluation of feature selection and demonstrates the potential of ensemble learning as a reliable approach for non-invasive diabetes detection.
Keywords Diabetes Prediction, Machine Learning, Feature Selection, Majority Voting, Ensemble Learning, Pima Indians Dataset
Field Computer > Data / Information
Published In Volume 16, Issue 3, July-September 2025
Published On 2025-09-11
DOI https://doi.org/10.71097/IJSAT.v16.i3.8175
Short DOI https://doi.org/g93xdt
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