Machine Learning Approaches for Obesity Classification and Prediction: An Analysis of Demographic, Lifestyle, and Health Factors

The high level of obesity poses a serious public health problem across the globe, being a leading cause of various chronic diseases and substantial threats to the quality of people's lives. Given the ever-growing rates of obesity and its impact on individual well-being, the current study aims to explore numerous factors that drive obesity, using machine learning algorithms to solve classification tasks. By working on a rich dataset that contains a range of demographic, lifestyle, and health parameters, several classifiers were developed and tested, namely Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, and K-Nearest Neighbors. The resulting outcomes indicated that both Random Forest and Gradient Boosting algorithms were highly accurate in the classification of obesity, with 95.0% and 95.3% accuracy rates, respectively. The obtained results confirm the critical role of various machine learning approaches in understanding obesity and developing predictions for more focused intervention. This study offers considerable input into obesity epidemiology literature and demonstrates the utility of advanced analytical appraisal in public health.