International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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OBESITY DETECTION

Author(s) Mr. VENU GOPAL N, Mr. KHURSHEED ABBAS M, Mr. LAKSHMI NARAYANA N, Mr. ESWAR BUNNY Y, Prof. Dr. SUJAY V
Country India
Abstract “OBESITY DETECTION” is a major global health concern associated with
numerous chronic diseases such as diabetes, cardiovascular conditions, and certain
forms of cancer. Early detection and monitoring of obesity are essential to prevent
long-term health complications. This study focuses on the development of a
reliable and efficient system for obesity detection using clinical data and machine
learning techniques. Key health parameters such as Body Mass Index (BMI), age,
dietary habits, physical activity levels, and medical history are analyzed to predict
obesity risk. Various machine learning algorithms, including decision trees, logistic
regression, and support vector machines, were evaluated to determine the most
accurate predictive model. The proposed system demonstrates high accuracy in
identifying individuals at risk and provides a valuable tool for healthcare
professionals to initiate early intervention. This research highlights the importance
of integrating artificial intelligence in public health to combat the obesity epidemic
through timely detection and personalized recommendations.
Keywords Obesity Detection, Computer Vision, Deep Learning, Convolutional Neural Networks (CNN), Body Mass Index (BMI), Image Classification, Health Monitoring, Human Body Analysis, Machine Learning, Medical Imaging.
Field Computer
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-05-16
DOI https://doi.org/10.36948/ijfmr.2025.v07i03.44767
Short DOI https://doi.org/g9kfs6
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