Obesity Health Risk Prediction using Random Forest and SVM Algorithms

Obesity-related health dangers have become a serious global issue that requires adequately pinpointing and quickly preventing health problems. One of the research studies in the article suggests a supervised learning-based framework that uses the Random Forest method for feature selection and the Support Vector Machine (SVM) technique for classification to predict obesity-induced health dangers. Aiming at increased accuracy in risk assessment, the framework involves the analysis of pertinent health and lifestyle parameters, including BMI, regular physical activity, and a proper diet. The solutions use machine learning as the main technical approach, which leads to real-time personal health recommendations and early medical interventions. Furthermore, the solution model ensures that it is scalable and adaptive, broadening the population to diverse patient demographics and thus contributing to proactive management of obesity conditions and achieving good patient outcomes.