Predicting the Risk of Overweight and Obesity in Madrid—A Binary Classification Approach with Evolutionary Feature Selection

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-...

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Published inApplied sciences Vol. 12; no. 16; p. 8251
Main Authors Parra, Daniel, Gutiérrez-Gallego, Alberto, Garnica, Oscar, Velasco, Jose Manuel, Zekri-Nechar, Khaoula, Zamorano-León, José J., Heras, Natalia de las, Hidalgo, J. Ignacio
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.08.2022
Subjects
Algorithms
Artificial intelligence
Body mass index
Body weight
Classification
Datasets
Evolution
evolutionary computing
Feature selection
genetic algorithm
Genetic algorithms
Learning algorithms
Lifestyles
Machine learning
Obesity
Overweight
Random variables
Spain
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12168251