Combination of Machine Learning Techniques to Predict Overweight/Obesity in Adults
Alberto Gutiérrez-Gallego, José J. Zamorano‐León, Daniel Parra, Khaoula Zekri-Nechar, J. Manuel Velasco, Óscar Garnica, Rodrigo Jiménez‐García, Ana López‐de‐Andrés, Natividad Cuadrado‐Corrales, David Carabantes-Alarcón, Vicente Lahera, Carlos Hugo Martínez-Martínez, J. Ignacio Hidalgo
Abstract
(1) Background: Artificial intelligence using machine learning techniques may help us to predict and prevent obesity. The aim was to design an interpretable prediction algorithm for overweight/obesity risk based on a combination of different machine learning techniques. (2) Methods: 38 variables related to sociodemographic, lifestyle, and health aspects from 1179 residents in Madrid were collected and used to train predictive models. Accuracy, precision, and recall metrics were tested and compared between nine classical machine learning techniques and the predictive model based on a combination of those classical machine learning techniques. Statistical validation was performed. The shapely additive explanation technique was used to identify the variables with the greatest impact on weight gain. (3) Results: Cascade classifier model combining gradient boosting, random forest, and logistic regression models showed the best predictive results for overweight/obesity compared to all machine learning techniques tested, reaching an accuracy of 79%, precision of 84%, and recall of 89% for predictions for weight gain. Age, sex, academic level, profession, smoking habits, wine consumption, and Mediterranean diet adherence had the highest impact on predicting obesity. (4) Conclusions: A combination of machine learning techniques showed a significant improvement in accuracy to predict risk of overweight/obesity than machine learning techniques separately.