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@article{191821,
        author = {Miss. Shubhangi Chendke and Miss. Rutika Khobragade and Mr. Samarth Nimkar and Mr. Krunal Wani},
        title = {Diabetes Prediction Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {8145-8149},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191821},
        abstract = {Diabetes mellitus is a chronic and life-threatening disease that requires early diagnosis to prevent severe complications. In recent years, machine learning (ML) techniques have been widely adopted to predict diabetes using clinical and lifestyle data. More recently, explainable artificial intelligence (XAI) has been introduced to improve transparency and trust in ML-based medical decision systems. This review paper presents a comprehensive and simple analysis of recent research works on diabetes prediction using machine learning, ensemble methods, and explainable Al techniques. This review paper presents a detailed and comprehensive analysis of diabetes prediction systems based on machine learning, ensemble learning, and explainable Al, using all uploaded research papers as primary sources. The review covers datasets, preprocessing methods, feature selection techniques, classification models, evaluation metrics, explainability approaches, and real-world deployment through web and mobile applications. Comparative analysis reveals that ensemble models such as XGBoost combined with imbalance-handling techniques like SMOTE and ADASYN, and explainability tools such as SHAP and LIME, achieve superior accuracy, interpretability, and clinical relevance. This review provides valuable insights for researchers, students, and healthcare professionals working on intelligent diabetes diagnosis systems.},
        keywords = {Diabetes Prediction, Machine Learning, Explainable Al, XGBoost, SHAP, LIME, SMOTE, ADASYN},
        month = {January},
        }