Estimation of Obesity Levels through the Proposed Predictive Approach Based on Physical Activity and Nutritional Habits

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dc.authoridYagin, Fatma Hilal/0000-0002-9848-7958
dc.authoridGulu, Mehmet/0000-0001-7633-7900
dc.authoridÇOLAK, CEMİL/0000-0001-5406-098X
dc.authoridGozukara Bag, Harika Gozde/0000-0003-1208-4072
dc.authoridPrieto-González, Pablo/0000-0002-0668-4031
dc.authoridArdigò, Luca Paolo/0000-0001-7677-5070
dc.authoridBadicu, Georgian/0000-0003-4100-8765
dc.authorwosidYagin, Fatma Hilal/ABI-8066-2020
dc.authorwosidgörmez, yasin/JEF-8096-2023
dc.authorwosidGulu, Mehmet/AAP-8658-2020
dc.authorwosidQuispe Calcina, Willian/JRX-9094-2023
dc.authorwosidÇOLAK, CEMİL/ABI-3261-2020
dc.authorwosidGozukara Bag, Harika Gozde/ABG-7588-2020
dc.authorwosidPrieto-González, Pablo/T-9113-2018
dc.contributor.authorGozukara Bag, Harika Gozde
dc.contributor.authorYagin, Fatma Hilal
dc.contributor.authorGormez, Yasin
dc.contributor.authorGonzalez, Pablo Prieto
dc.contributor.authorColak, Cemil
dc.contributor.authorGulu, Mehmet
dc.contributor.authorBadicu, Georgian
dc.date.accessioned2024-08-04T20:54:42Z
dc.date.available2024-08-04T20:54:42Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractObesity is the excessive accumulation of adipose tissue in the body that leads to health risks. The study aimed to classify obesity levels using a tree-based machine-learning approach considering physical activity and nutritional habits. Methods: The current study employed an observational design, collecting data from a public dataset via a web-based survey to assess eating habits and physical activity levels. The data included gender, age, height, weight, family history of being overweight, dietary patterns, physical activity frequency, and more. Data preprocessing involved addressing class imbalance using Synthetic Minority Over-sampling TEchnique-Nominal Continuous (SMOTE-NC) and feature selection using Recursive Feature Elimination (RFE). Three classification algorithms (logistic regression (LR), random forest (RF), and Extreme Gradient Boosting (XGBoost)) were used for obesity level prediction, and Bayesian optimization was employed for hyperparameter tuning. The performance of different models was evaluated using metrics such as accuracy, recall, precision, F1-score, area under the curve (AUC), and precision-recall curve. The LR model showed the best performance across most metrics, followed by RF and XGBoost. Feature selection improved the performance of LR and RF models, while XGBoost's performance was mixed. The study contributes to the understanding of obesity classification using machine-learning techniques based on physical activity and nutritional habits. The LR model demonstrated the most robust performance, and feature selection was shown to enhance model efficiency. The findings underscore the importance of considering both physical activity and nutritional habits in addressing the obesity epidemic.en_US
dc.description.sponsorshipThe authors would like to thank Prince Sultan University for their support.; Prince Sultan Universityen_US
dc.description.sponsorshipThe authors would like to thank Prince Sultan University for their support.en_US
dc.identifier.doi10.3390/diagnostics13182949
dc.identifier.issn2075-4418
dc.identifier.issue18en_US
dc.identifier.pmid37761316en_US
dc.identifier.scopus2-s2.0-85172181338en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/diagnostics13182949
dc.identifier.urihttps://hdl.handle.net/11616/101586
dc.identifier.volume13en_US
dc.identifier.wosWOS:001074377200001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofDiagnosticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectobesityen_US
dc.subjectmachine learningen_US
dc.subjectphysical activityen_US
dc.subjectnutritional habitsen_US
dc.subjectclassificationen_US
dc.titleEstimation of Obesity Levels through the Proposed Predictive Approach Based on Physical Activity and Nutritional Habitsen_US
dc.typeArticleen_US

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