A Survey on Machine Learning approach for Early Detection and Prevention of Obesity and Overweight
Prof. M.K. Nivangune, Simran Sayyad, Mrunal Shewale, Prachi Rane, Prerana Madhavi
Obesity; Overweight; Prediction; Prevention; Desktop application; Machine Learning
Obesity stands as a pressing global crisis, affecting more than 2.1 billion individuals, with projections indicating that 41% of the global population could be overweight or obese by 2030. This widespread issue poses severe health hazards, including diabetes and cardiovascular complications. To combat this epidemic, a comprehensive dataset has been meticulously crafted utilizing information from educational institutions, centering on Body Mass Index (BMI) as a key metric. Early identification holds paramount importance for implementing timely interventions, such as lifestyle modifications. A systematic framework has been devised to forecast BMI and body fat percentage, alongside offering personalized preventive strategies. Furthermore, real-time detection mechanisms have been integrated to swiftly ascertain an individual's obesity status. Leveraging data gathered from schools and colleges, effective models for obesity detection and prevention have been developed. The outcomes are consolidated and showcased through a desktop application, enriched with an array of preventive measures and calculators.
Article Details
Unique Paper ID: 163196

Publication Volume & Issue: Volume 10, Issue 11

Page(s): 868 - 872
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