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

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dc.authoridArdigo, Luca Paolo/0000-0001-7677-5070
dc.authoridPrieto Gonzalez, Dr. Pablo/0000-0002-0668-4031
dc.authoridgormez, yasin/0000-0001-8276-2030
dc.authoridCOLAK, Cemil/0000-0001-5406-098X
dc.authoridBadicu, Georgian/0000-0003-4100-8765
dc.authoridGulu, Mehmet/0000-0001-7633-7900
dc.authoridYAGIN, Fatma Hilal/0000-0002-9848-7958
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.accessioned2025-01-21T16:40:35Z
dc.date.available2025-01-21T16:40:35Z
dc.date.issued2023
dc.departmentKırıkkale Üniversitesi
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.
dc.description.sponsorshipThe authors would like to thank Prince Sultan University for their support.; Prince Sultan University
dc.description.sponsorshipThe authors would like to thank Prince Sultan University for their support.
dc.identifier.doi10.3390/diagnostics13182949
dc.identifier.issn2075-4418
dc.identifier.issue18
dc.identifier.pmid37761316
dc.identifier.scopus2-s2.0-85172181338
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/diagnostics13182949
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24707
dc.identifier.volume13
dc.identifier.wosWOS:001074377200001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofDiagnostics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectobesity; machine learning; physical activity; nutritional habits; classification
dc.titleEstimation of Obesity Levels through the Proposed Predictive Approach Based on Physical Activity and Nutritional Habits
dc.typeArticle

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