Estimation of obesity levels with a trained neural network approach optimized by the Bayesian technique

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dc.contributor.authorYagin, Fatma Hilal
dc.contributor.authorGülü, Mehmet
dc.contributor.authorGormez, Yasin
dc.contributor.authorCastañeda Babarro, Arkaitz
dc.contributor.authorColak, Cemil
dc.contributor.authorGreco, Gianpero
dc.contributor.authorFischetti, Francesco
dc.contributor.authorCataldi, Stefania
dc.date.accessioned2025-06-06T15:57:37Z
dc.date.available2025-06-06T15:57:37Z
dc.date.issued2023-03-18
dc.date.updated2025-06-06T15:57:37Z
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
dc.identifier.citationYagin, F. H., Gülü, M., Gormez, Y., Castañeda-Babarro, A., Colak, C., Greco, G., Fischetti, F., & Cataldi, S. (2023). Estimation of obesity levels with a trained neural network approach optimized by the Bayesian technique. Applied Sciences (Switzerland), 13(6). https://doi.org/10.3390/APP13063875
dc.identifier.doi10.3390/APP13063875
dc.identifier.eissn2076-3417
dc.identifier.urihttp://hdl.handle.net/20.500.14454/2965
dc.language.isoeng
dc.publisherMDPI
dc.rights© 2023 by the authors
dc.subject.otherBayesian optimization
dc.subject.otherEating habits
dc.subject.otherMachine learning
dc.subject.otherNeural network
dc.subject.otherObesity
dc.subject.otherPhysical activity
dc.titleEstimation of obesity levels with a trained neural network approach optimized by the Bayesian techniqueen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue6
oaire.citation.titleApplied Sciences (Switzerland)
oaire.citation.volume13
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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