Prediction of Obesity Levels Based on Physical Activity and Eating Habits with a Machine Learning Model Integrated with Explainable Artificial Intelligence

Yasin Görmez¹Fatma Hilal Yagin²Burak Yagin²Yalin Aygun³Hulusi Boke⁴Georgian Badicu⁵Matheus Santos De Sousa Fernandes^6,7Abedalrhman Alkhateeb⁸Mahmood Basil A. Al-Rawi⁹Mohammadreza Aghaei^10,11*

• ¹Department of Management Information Systems, Faculty of Economics and Administrative Sciences, Sivas Cumhuriyet University, Sivas, Sivas, Türkiye
• ²Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya, Türkiye
• ³Department of Sport Management, Faculty of Sport Sciences, Inonu University,, Inohu, Türkiye
• ⁴Yasar Oncan Secondary School, Ministry of National Education, 44900 Malatya, Malatya, Türkiye
• ⁵Department of Physical Education and Special Motricity, Faculty of Physical Education and Mountain Sports, Transilvania University of Brașov, Brasov, Brasov, Romania
• ⁶Federal University of Pernambuco, Recife, Brazil
• ⁷Keizo Asami Institute, Federal University of Pernambuco, Pernambuco, Brazil
• ⁸Department of Computer Science, Lakehead University, Thunder Bay, ON P7B 5E1, Canada, Ontario, Canada
• ⁹Department of Optometry, College of Applied Medical Sciences, King Saud University, Riyadh, Riyadh, Saudi Arabia
• ¹⁰Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), Alesund, Alesund, Norway
• ¹¹Department of Sustainable Systems Engineering (INATECH), Albert Ludwigs University of Freiburg, Freiburg, Freiburg, Germany

Objectives: This study aims to build a machine learning (ML) prediction model integrated with explainable artificial intelligence (XAI) to categorize obesity levels from physical activity and dietary patterns. The inclusion of XAI methodologies facilitates a comprehensive understanding of the risk factors influencing the model predictions and thus increases transparency in the identification of obesity risk factors. Methods: Six ML models were used: Bernoulli Naive Bayes, CatBoost, Decision Tree, Extra Trees Classifier, Histogram-based Gradient Boosting and Support Vector Machine. For each model, hyperparameters were tuned by random search methodology and model effectiveness was evaluated by repeated holdout testing. SHAP (SHapley Additive Annotations) and LIME (Local Interpretable Model Independent Annotations) interpretability methods were used to generate local and global feature importance measures. Results: The CatBoost model exhibited the highest overall performance and achieved superior results in accuracy, precision, F1 score and AUC metrics. Nonetheless, other models such as Decision Tree and Histogram-based Gradient Boosting also yielded strong and competitive results. The results also highlighted age, weight, height and specific food patterns as key predictors of obesity. In terms of interpretability, LIME showed superior in fidelity,, whereas SHAP showed improved sparsity and consistency across models, facilitating a comprehensive understanding of trait importance. Conclusion: This research demonstrates that ML algorithms, when integrated with XAI technologies, can accurately predict obesity levels and explain important contributing risk factors. The use of SHAP and LIME increases model transparency, facilitating the identification of specific lifestyle patterns linked to obesity risk. These findings help to formulate more precise intervention techniques guided by a reliable and understandable predictive framework.