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AI Driven Body Composition Risk Assessment Model

Dr.SHANMUGAPRIYA VELMURUGAN1 ~MOHAMMED SHAMEER.K2 ~ TAMIL SELVAN.V 3
1 Assistant Professor Department of Computer Science,
2UG Student (III B.Sc. Computer Science), Department of Computer Science,
3 UG Student (III B.Sc. Computer Science), Department of Computer Science,
Sri Krishna Arts and Science College, Coimbatore, Tamil Nadu, India
tamilselvanv23bcs059@skasc.ac.in, mohammedshameerk23bcs033@skasc.ac.in, shanmugapriyav@skasc.ac.in

Abstract—This project is entitled as “AI DRIVEN BODY COMPOSITION RISK ASSESSMENT MODEL”. Overweight is a major health concern influenced by dietary habits, physical activity, and lifestyle behaviours. Predicting an individual’s weight condition at an early stage can help in promoting healthier living and reducing future health risks. This project focuses on overweight prediction by identifying whether a person falls under low weight, normal weight, or overweight categories using machine learning techniques. The dataset includes attributes such as age, gender, height, weight, family history, food consumption patterns, water intake, physical activity, smoking habits, alcohol consumption, and daily lifestyle activities. A Random Forest algorithm is employed to analyze the relationship between these factors and body weight outcomes, as it provides reliable performance with diverse input features. The developed model predicts the weight conditioneffectively based on personal and lifestyle information, supporting health awareness and informed decision making related to overweight prevention.