Prediction and classification of obesity risk based on a hybrid metaheuristic machine learning approach

Abstract

Introduction: As the global prevalence of obesity continues to rise, it has become a major public health concern requiring more accurate prediction methods. Traditional regression models often fail to capture the complex interactions between genetic, environmental, and behavioral factors contributing to obesity. Methods: This study explores the potential of machine-learning techniques to improve obesity risk prediction. Various supervised learning algorithms, including the novel ANN-PSO hybrid model, were applied following comprehensive data preprocessing and evaluation. Results: The proposed ANN-PSO model achieved a remarkable accuracy rate of 92%, outperforming traditional regression methods. SHAP was employed to analyze feature importance, offering deeper insights into the influence of various factors on obesity risk. Discussion: The findings highlight the transformative role of advanced machine-learning models in public health research, offering a pathway for personalized healthcare interventions. By providing detailed obesity risk profiles, these models enable healthcare providers to tailor prevention and treatment strategies to individual needs. The results underscore the need to integrate innovative machine-learning approaches into global public health efforts to combat the growing obesity epidemic.

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