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@article{162796,
        author = {Shraddha Borase and Vaishnavi Bhoi and Vaishnavi patil and Umesh Patel},
        title = {Obesity Detection Using Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {11},
        pages = {20-25},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=162796},
        abstract = {Obesity has become a global health concern, with its prevalence reaching alarming levels in recent years. Effective management of obesity requires a clear understanding of its classification, which enables healthcare professionals to tailor interventions and develop personalized treatment strategies. This review aims to provide a comprehensive overview of obesity classification systems, highlighting their strengths, limitations, and implications for clinical practice. The review begins by discussing the commonly used anthropometric measures for assessing obesity, such as body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR). It explores the advantages and drawbacks of these measures, including their inability to accurately account for variations in body composition and distribution of adipose tissue. For this research, we apply prominent machine learning algorithms. We used the algorithm of random forest, logistic regression, Decision Tree, support vector machine (SVM), and we have measured the performance of each of these classifications in terms of some prominent performance metrics. From the experimental results, we determine the obesity of high, medium, and low.},
        keywords = {Diagnosis, DT, Machine Learning, RF, SVM.},
        month = {},
        }