A Survey Paper on Obesity Detection

Obesity is a global health epidemic with significant implications for public health. Timely and accurate detection of obesity is crucial for effective prevention and intervention. This abstract provides an overview of the current state of obesity detection methods, their challenges, and the potential role of emerging technologies in addressing these issues. Traditional methods of obesity detection primarily rely on body mass index (BMI) calculations, which have limitations in accurately assessing body composition and health risks associated with obesity. More advanced approaches, such as dual-energy X-ray absorptiometry (DXA), bioelectrical impedance analysis (BIA), and magnetic resonance imaging (MRI), offer improved accuracy in assessing body fat percentage, but they are often costly and less accessible in routine clinical settings. Support Vector Machine, Decision Trees, Random Forest and Logistic Regression can be used for classification. Recent developments in artificial intelligence and machine learning have shown promise in automating and enhancing obesity detection. These technologies can process diverse data sources, including medical imaging, electronic health records, and wearable devices, to provide a more comprehensive and personalized assessment of an individual’s obesity status. Machine learning models can also integrate multiple factors, such as genetics, diet, and physical activity, to offer a holistic view of obesity risk.