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@article{175495,
        author = {Dr.K.Venkata Nagendra and Dr.Krishna Prasad},
        title = {AN IN-DEPTH ANALYSIS OF OBESITY PREDICTION THROUGH MACHINE LEARNING METHODS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {3098-3106},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175495},
        abstract = {Obesity represents a significant public health issue, as it is linked to various health conditions, including diabetes, cardiovascular diseases, and certain types of cancer. The Body Mass Index (BMI), which is calculated by dividing an individual's weight in kilograms by the square of their height in meters, is the primary method employed to diagnose obesity. Individuals with a BMI exceeding 30 kg/m² are categorized as obese, while those with a BMI over 25 kg/m² are classified as overweight. Despite its widespread use due to its simplicity and affordability, BMI has limitations, particularly in its inability to consider variations in muscle mass, bone density, and fat distribution. Consequently, alternative assessment methods such as skinfold thickness measurement, bioelectrical impedance analysis, and dual-energy X-ray absorptiometry (DXA) are utilized to evaluate body fat and muscle mass. The research indicated that the Random Forest model (RF) achieved the highest accuracy rate of 95.78%. Other algorithms MLP, SVM, RS, NB and DT also yielded commendable accuracy results. This study implies that the practical implementation of the model could assist healthcare professionals in identifying individuals who are overweight or obese, thereby facilitating the early detection, prevention, and management of obesity-related health issues.},
        keywords = {Obesity Detection, Obesity Prevention, Eating habits, Classification, Machine Learning, Body Mass Index, Rough Set, Support Vector Machines.},
        month = {April},
        }