Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 3 , Issue 2 , PP: 08-17, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Nima Khodadadi 1 * , Mohamed Saber 2 , Mostafa Abotaleb 3
Doi: https://doi.org/10.54216/JAIM.030201
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.
Machine Learning , Obesity Classification , Artificial Intelligence (AI) , Data-Driven Intelligence.
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