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@article{180750,
        author = {Mr. Nitesh Kumar and Mr. Nitin Kumar},
        title = {Diabetes Detection Using Machine Learning: A Comparative Analysis of Classification Algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {2304-2309},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180750},
        abstract = {This research presents a comprehensive comparison of four machine learning classification algorithms—K-Nearest Neighbors (KNN), Decision Tree, Random Forest, and Naive Bayes—for predicting diabetes occurrence. Using a dataset containing 2,000 patient records with various health parameters, we implement a complete machine learning pipeline including data preprocessing, feature analysis, model development, and performance evaluation. The experimental results demonstrate that Random Forest achieved the highest accuracy of 100%, followed by Decision Tree (99.25%), KNN (80.25%), and Naive Bayes (76.5%). This comparative analysis provides insights into the effectiveness of different classification algorithms for diabetes prediction and highlights the potential of machine learning in healthcare diagnostics.},
        keywords = {Machine Learning, Diabetes Prediction, Classification Algorithms, Healthcare Analytics, Random Forest, Decision Tree},
        month = {June},
        }