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@article{176302,
        author = {NAVANEETH H and MYTHILI N and Mr.M.Vijayakumar, Assistant Professor},
        title = {KNN BASED CHRONIC DISEASE PREDICTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {7015-7020},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176302},
        abstract = {Chronic diseases such as diabetes, heart disease, and kidney disorders are among the leading causes of mortality worldwide, necessitating early detection and effective monitoring. This study presents a machine learning approach utilizing the K-Nearest Neighbors (KNN) algorithm for the prediction of chronic diseases based on clinical and demographic data. The model is trained and evaluated using publicly available healthcare datasets, incorporating features such as age, blood pressure, glucose levels, BMI, and other relevant indicators. KNN, known for its simplicity and effectiveness in classification problems, is employed to identify patterns and similarities between patient profiles. The results demonstrate that the KNN model achieves promising accuracy, precision, and recall, making it a viable tool for supporting early diagnosis in clinical settings. This approach can aid healthcare professionals in making informed decisions and potentially reduce the burden of chronic illnesses through timely intervention.},
        keywords = {},
        month = {April},
        }