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ESEDPM-V2: An Explainable Hybrid Ensemble Framework for Diabetes Prediction Using Machine Learning, Deep Learning and Explainable Artificial Intelligence
Authors
KIRAN KUMAR YELIJALA
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Keywords
Keywords: Diabetes Prediction, Explainable Artificial Intelligence, Ensemble Learning, Machine Learning, Deep Learning, CatBoost, LightGBM, XGBoost, SHAP, LIME, Healthcare Analytics.
Abstract
Diabetes mellitus is a chronic metabolic disorder that continues to pose a significant public health challenge worldwide. Early identification of individuals at risk of developing diabetes is essential for reducing disease-related complications and improving healthcare outcomes. Recent developments in artificial intelligence have enabled the use of machine learning and deep learning techniques for disease prediction; however, many existing predictive models lack transparency and interpretability, limiting their practical adoption in clinical environments.
This study proposes an Explainable Ensemble Diabetes Prediction Model Version 2 (ESEDPM-V2), a hybrid framework that combines advanced machine learning, deep learning, and explainable artificial intelligence techniques for diabetes prediction. The proposed framework integrates CatBoost, LightGBM, XGBoost, and Artificial Neural Networks (ANN) through a stacking-based ensemble architecture. To improve data quality and predictive capability, several preprocessing techniques were employed, including missing value treatment, feature engineering, class balancing using Synthetic Minority Oversampling Technique (SMOTE), and feature scaling. Furthermore, Explainable Artificial Intelligence (XAI) methods such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) were incorporated to provide both global and local explanations for prediction outcomes.
Experimental evaluation was conducted using the Pima Indians Diabetes Dataset. The proposed ESEDPM-V2 framework achieved an accuracy of 77.92%, outperforming several conventional machine learning models and demonstrating competitive predictive performance. Explainability analysis revealed that engineered features such as Glucose_BMI, Glucose_Age, DiabetesPedigreeFunction, and Glucose were among the most influential predictors of diabetes risk. The findings indicate that combining ensemble learning with explainable artificial intelligence can improve prediction reliability while maintaining transparency and interpretability. The proposed framework may serve as a supportive decision-making tool for healthcare professionals involved in diabetes screening and risk assessment
IJCRT's Publication Details
Unique Identification Number - IJCRT2606609
Paper ID - 310832
Author type - Indian Author
Page Number(s) - f676-f688
Pubished in - Volume 14 | Issue 6 | June 2026
DOI (Digital Object Identifier) - https://doi.org/10.56975/ijcrt.v14i6.310832
No Of Downloads - 84
Author Country - India, 508001, NALGONDA, NALGONDA, 508001, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
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KIRAN KUMAR YELIJALA, "ESEDPM-V2: An Explainable Hybrid Ensemble Framework for Diabetes Prediction Using Machine Learning, Deep Learning and Explainable Artificial Intelligence", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.14, Issue 6, pp.f676-f688, June 2026, Available at :http://www.ijcrt.org/papers/IJCRT2606609.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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