Predicting Obesity in Nutritional Patients using Decision Tree Modeling

• Autores: Iparraguirre-Villanueva, Orlando, Mirano-Portilla, Luis, Gamarra-Mendoza, Manuel, Robles-Espiritu, Wilmer
• Formato: artículo
• Fecha de Publicación: 2023
• Institución: Universidad Autónoma del Perú
• Repositorio: AUTONOMA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.autonoma.edu.pe:20.500.13067/3187
• Enlace del recurso: https://hdl.handle.net/20.500.13067/3187; https://doi.org/10.14569/IJACSA.2024.0150326
• Nivel de acceso: acceso abierto
• Materia: Obesity; Machine Learning (ML); Decision Tree (DT); Prediction; CRISP-DM; https://purl.org/pe-repo/ocde/ford#2.02.04

Obesity has become a widespread problem that affects not only physical well-being but also mental health. To address this problem and provide solutions, Machine Learning (ML) technology tools are being applied. Studies are currently being developed to improve the prediction of obesity. This study aimed to predict obesity levels in nutritional patients by analyzing their physical and dietary habits using the Decision Tree (DT) model. For the development of this work, we chose to use the CRISP-DM framework to follow the development in an organized way, thus achieving a better understanding of the data and describing, evaluating, and analyzing the results. The results of this work yielded metrics with significant values for predicting obesity: so much so that the accuracy rate was 92.89%, the sensitivity rate was 94% and the F1 score was 93%. Likewise, accuracy metrics above 88% were obtained for each level of obesity, demonstrating the effectiveness of the DT model in predicting this type of task. Finally, the results demonstrate that the DT model is effective in predicting obesity, with significant results that motivate further research to continue improving accuracy in this type of task.