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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Prajakta Bhosale-Dhamdhere
Design and Technology University
Computer Science & IT Engineering

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Performance-optimized boosted hybrid ensemble model for diabetes risk prediction Prajakta Bhosale-Dhamdhere; Ganesh Pathak
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2062-2080

Abstract

The proposed boosted hybrid ensemble (BHE) machine learning (ML) model utilizes the classification power which reduces the overfitting by bagging and generates better results using random forest (RF) and extreme gradient boosting (XGBoost). The paper presents the importance and impact of secondary features in type 2 diabetes prediction utilizing real-time self reported and hospital data. The research study shows that age, gender, body mass index (BMI), and glucose are the key prime factors and are also influence by the other factors like demographic conditions, eating, and activity styles to some extents. The paper presents transfer learning (TL) on the basis on standard Pima Indians diabetes dataset (PIMA) to apply hybrid 2-layer BHE model to predict and classify the records into diabetic and non diabetic class providing explanations to factors contributing to it. The result section shows the highest 98% accuracy for BHE with optimized model presenting recommendations as per careful considerations of World Health Organization (WHO) and American Diabetes Association (ADA) standards. The paper throws light on the need of life-style factors considerations and correction to establish causation and refine preventive strategies in avoiding or postponing type-2 occurrences in youth people. This paper present perfect integration of multifactorial data with high reliability of artificial intelligence (AI)-driven healthcare explainable models to generate recommendations utilizing TLs.
Co-Authors Ganesh Pathak