1. Predicting the Risk of Overweight and Obesity in Madrid—A Binary Classification Approach with Evolutionary Feature Selection
- Author
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Daniel Parra, Alberto Gutiérrez-Gallego, Oscar Garnica, Jose Manuel Velasco, Khaoula Zekri-Nechar, José J. Zamorano-León, Natalia de las Heras, and J. Ignacio Hidalgo
- Subjects
Fluid Flow and Transfer Processes ,Process Chemistry and Technology ,General Engineering ,General Materials Science ,Instrumentation ,feature selection ,classification ,genetic algorithm ,evolutionary computing ,overweight ,obesity ,Computer Science Applications - Abstract
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.
- Published
- 2022