Estimation of Obesity Levels with a Trained Neural Network Approach optimized by the Bayesian Technique

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dc.authoridCastaneda-Babarro, Arkaitz/0000-0002-4568-320X
dc.authoridGulu, Mehmet/0000-0001-7633-7900
dc.authoridCOLAK, Cemil/0000-0001-5406-098X
dc.authoridGreco, Gianpiero/0000-0002-5023-3721
dc.authoridYAGIN, Fatma Hilal/0000-0002-9848-7958
dc.authoridgormez, yasin/0000-0001-8276-2030
dc.authoridFrancesco, Fischetti/0000-0001-8616-5372
dc.contributor.authorYagin, Fatma Hilal
dc.contributor.authorGulu, Mehmet
dc.contributor.authorGormez, Yasin
dc.contributor.authorCastaneda-Babarro, Arkaitz
dc.contributor.authorColak, Cemil
dc.contributor.authorGreco, Gianpiero
dc.contributor.authorFischetti, Francesco
dc.date.accessioned2025-01-21T16:40:35Z
dc.date.available2025-01-21T16:40:35Z
dc.date.issued2023
dc.departmentKırıkkale Üniversitesi
dc.description.abstractBackground: Obesity, which causes physical and mental problems, is a global health problem with serious consequences. The prevalence of obesity is increasing steadily, and therefore, new research is needed that examines the influencing factors of obesity and how to predict the occurrence of the condition according to these factors. This study aimed to predict the level of obesity based on physical activity and eating habits using the trained neural network model. Methods: The chi-square, F-Classify, and mutual information classification algorithms were used to identify the most critical factors associated with obesity. The models' performances were compared using a trained neural network with different feature sets. The hyperparameters of the models were optimized using Bayesian optimization techniques, which are faster and more effective than traditional techniques. Results: The results predicted the level of obesity with average accuracies of 93.06%, 89.04%, 90.32%, and 86.52% for all features using the neural network and for the features selected by the chi-square, F-Classify, and mutual information classification algorithms. The results showed that physical activity, alcohol consumption, use of technological devices, frequent consumption of high-calorie meals, and frequency of vegetable consumption were the most important factors affecting obesity. Conclusions: The F-Classify score algorithm identified the most essential features for obesity level estimation. Furthermore, physical activity and eating habits were the most critical factors for obesity prediction.
dc.identifier.doi10.3390/app13063875
dc.identifier.issn2076-3417
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85151526931
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/app13063875
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24708
dc.identifier.volume13
dc.identifier.wosWOS:000954090900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofApplied Sciences-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectobesity; physical activity; eating habits; machine learning; neural network; Bayesian optimization
dc.titleEstimation of Obesity Levels with a Trained Neural Network Approach optimized by the Bayesian Technique
dc.typeArticle

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