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

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dc.authoridGreco, Gianpiero/0000-0002-5023-3721
dc.authoridÇOLAK, CEMİL/0000-0001-5406-098X
dc.authoridGulu, Mehmet/0000-0001-7633-7900
dc.authoridCastañeda-Babarro, Arkaitz/0000-0002-4568-320X
dc.authoridYagin, Fatma Hilal/0000-0002-9848-7958
dc.authoridgormez, yasin/0000-0001-8276-2030
dc.authoridCataldi, Stefania/0000-0002-5929-4766
dc.authorwosidGreco, Gianpiero/F-8992-2019
dc.authorwosidÇOLAK, CEMİL/ABI-3261-2020
dc.authorwosidGulu, Mehmet/AAP-8658-2020
dc.authorwosidQuispe Calcina, Willian/JRX-9094-2023
dc.authorwosidCastañeda-Babarro, Arkaitz/AAM-4239-2021
dc.authorwosidYagin, Fatma Hilal/ABI-8066-2020
dc.authorwosidgörmez, yasin/JEF-8096-2023
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.accessioned2024-08-04T20:53:35Z
dc.date.available2024-08-04T20:53:35Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
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.en_US
dc.identifier.doi10.3390/app13063875
dc.identifier.issn2076-3417
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85151526931en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/app13063875
dc.identifier.urihttps://hdl.handle.net/11616/101272
dc.identifier.volume13en_US
dc.identifier.wosWOS:000954090900001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofApplied Sciences-Baselen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectobesityen_US
dc.subjectphysical activityen_US
dc.subjecteating habitsen_US
dc.subjectmachine learningen_US
dc.subjectneural networken_US
dc.subjectBayesian optimizationen_US
dc.titleEstimation of Obesity Levels with a Trained Neural Network Approach optimized by the Bayesian Techniqueen_US
dc.typeArticleen_US

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