Gutiérrez-Gallego, AlbertoZekri-Nechar, KhaoulaZamorano-León, José J.Heras, Natalia De lasParra Rodríguez, DanielVelasco Cabo, José ManuelGarnica Alcázar, Antonio ÓscarHidalgo Pérez, José Ignacio2025-01-302025-01-302022-08-182076-341710.3390/app12168251https://hdl.handle.net/20.500.14352/117368In 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.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/journal articleopen accessFeature selectionClassificationGenetic algorithmEvolutionary computingOverweight; obesityInformática (Informática)33 Ciencias Tecnológicas