Multi Algorithm Based Obesity Analysis System Techniques

Obesity is a rapidly growing global health challenge, contributing significantly to the burden of non-communicable diseases such as diabetes, cardiovascular disorders, and joint problems. Traditional obesity assessment methods, predominantly based on Body Mass Index (BMI) and other basic anthropometric measurements, often fail to provide a holistic understanding of the condition due to their inability to incorporate multiple contributing factors. This project presents a Multi-Algorithm-Based Obesity Analysis System that leverages machine learning and statistical techniques to enhance the accuracy and depth of obesity prediction and risk assessment. By integrating data from various domains—including physical activity, dietary habits, genetic predispositions, psychological well-being, and clinical parameters—the system employs a range of algorithms such as Decision Trees, Support Vector Machines, and Neural Networks to uncover complex patterns and interactions within the data. The architecture fuses both phenotyping and genotyping inputs to offer a personalized, real-time risk analysis and intervention strategy. This intelligent system empowers healthcare professionals, researchers, and individuals by providing actionable insights, thus facilitating early prevention and effective obesity management [4].