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@article{208952,
        author = {Punit C. Trivedi and Ritesh B. Vaghasiya},
        title = {Machine Learning and Explainable Artificial Intelligence for Diabetes Prediction: A Review of Pre-processing, Feature Optimization, Model Performance, Risk Assessment, and Personalized Healthcare},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {5},
        pages = {145-152},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=208952},
        abstract = {Diabetes Mellitus is a major chronic metabolic disorder for which early prediction can support preventive healthcare. Machine Learning (ML) has been widely applied to diabetes prediction using clinical and patient health data. This paper reviews recent ML-based diabetes prediction studies with emphasis on datasets, pre-processing, feature optimization, classification algorithms, ensemble learning, model optimization, Explainable Artificial Intelligence (XAI), risk assessment, and healthcare recommendations. The review indicates that optimized and ensemble ML models generally achieve competitive prediction performance, while SHAP and LIME are increasingly used for model interpretation. However, most existing studies focus mainly on prediction performance, with limited integration of explainability, multi-level risk assessment, and personalized preventive recommendations. The review highlights the need for integrated and interpretable ML-based diabetes decision-support frameworks.},
        keywords = {Diabetes Mellitus, Machine Learning, Diabetes Prediction, Explainable Artificial Intelligence, SHAP, LIME, Ensemble Learning, Risk Assessment, Healthcare Recommendations.},
        month = {October},
        }