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@article{175910,
        author = {Dr.K.Venkata Nagendra and Dr. Praveen B M},
        title = {PERFORMANCE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR PREDICTING OBESITY RISK},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4520-4525},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175910},
        abstract = {Obesity has emerged as a significant global health issue, leading to a variety of chronic illnesses and a diminished quality of life. Early and precise assessment of obesity risk can greatly improve preventive healthcare measures. This research offers a comparative study of different supervised machine learning algorithms aimed at identifying individuals susceptible to obesity based on their lifestyle and physiological characteristics. The models analyzed include Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Gradient Boosting, and XGBoost, all of which were trained and assessed using a well-structured dataset. Evaluation metrics were employed to assess the performance of the models. The findings indicate that ensemble methods, particularly XGBoost, surpass traditional classifiers in both predictive accuracy and reliability.},
        keywords = {Obesity Prediction, Machine Learning, Supervised Learning, K-Nearest Neighbors, Support Vector Machine, XG Boost, Random Forest.},
        month = {April},
        }