RT Journal Article
T1 Predicting the Risk of Overweight and Obesity in Madrid : A Binary Classification Approach with Evolutionary Feature Selection
A1 Gutiérrez-Gallego, Alberto
A1 Zekri-Nechar, Khaoula
A1 Zamorano-León, José J.
A1 Heras, Natalia De las
A1 Parra Rodríguez, Daniel
A1 Velasco Cabo, José Manuel
A1 Garnica Alcázar, Antonio Óscar
A1 Hidalgo Pérez, José Ignacio
AB 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.
PB MDPI
SN 2076-3417
YR 2022
FD 2022-08-18
LK https://hdl.handle.net/20.500.14352/117368
UL https://hdl.handle.net/20.500.14352/117368
LA eng
DS Docta Complutense
RD 8 jun 2025