%0 Journal Article
%A Gutiérrez-Gallego, Alberto
%A Zekri-Nechar, Khaoula
%A Zamorano-León, José J.
%A Heras, Natalia De las
%A Parra Rodríguez, Daniel
%A Velasco Cabo, José Manuel
%A Garnica Alcázar, Antonio Óscar
%A Hidalgo Pérez, José Ignacio
%T Predicting the Risk of Overweight and Obesity in Madrid : A Binary Classification Approach with Evolutionary Feature Selection
%D 2022
%@ 2076-3417
%U https://hdl.handle.net/20.500.14352/117368
%X In this paper, we experimented with a set of machine-learning classifiers for predicting the risk of a person being overweight or obese, taking into account his/her dietary habits and socioeconomic information. We investigate with ten different machine-learning algorithms combined with four feature-selection strategies (two evolutionary feature-selection methods, one feature selection from the literature, and no feature selection). We tackle the problem under a binary classification approach with evolutionary feature selection. In particular, we use a genetic algorithm to select the set of variables (features) that optimize the accuracy of the classifiers. As an additional contribution, we designed a variant of the Stud GA, a particular structure of the selection operator of individuals where a reduced set of elitist solutions dominate the process. The genetic algorithm uses a direct binary encoding, allowing a more efficient evaluation of the individuals. We use a dataset with information from more than 1170 people in the Spanish Region of Madrid. Both evolutionary and classical feature-selection methods were successfully applied to Gradient Boosting and Decision Tree algorithms, reaching values up to 79% and increasing the average accuracy by two points, respectively.
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