Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 10.54216/JAIM https://www.americaspg.com/journals/show/1904 2022 2022 Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL, USA Nima Khodadadi Electronics and Communications Engineering Department, Faculty of Engineering, Delta University for Science and Technology, Gamasa City 11152, Egypt Mohamed Saber Department of System Programming, South Ural State University, 454080 Chelyabinsk, Russia Mostafa Abotaleb Obesity is a global health concern with significant impacts on individuals and society. Accurate and timely classification of obesity levels can help in the development of personalized interventions and targeted healthcare strategies. In this paper, we propose a data-driven approach for obesity classification utilizing machine learning techniques. Our study leverages a comprehensive dataset consisting of anthropometric measurements, lifestyle factors, and demographic information of a large cohort of individuals. We explore the effectiveness of various machine learning algorithms, including decision trees, support vector machines, and neural networks, for obesity classification. Feature selection and preprocessing techniques are employed to enhance the performance of the models. Through extensive experimentation and cross-validation, we evaluate the predictive accuracy, sensitivity, specificity, and overall performance of the developed classifiers. Our results demonstrate the efficacy of our data-driven approach, achieving high accuracy in obesity classification. Furthermore, we conduct a comparative analysis of the different algorithms to identify the most suitable model for this task. The proposed framework has the potential to assist healthcare professionals in identifying and classifying obesity levels accurately, contributing to the development of personalized interventions and improving public health outcomes. 2023 2023 08 17 10.54216/JAIM.030201 https://www.americaspg.com/articleinfo/28/show/1904