Research Article
Comparison of Classification Results of Categorical Boosting and Extreme Gradient Boosting Methods in Obesity Class Diagnosis
@INPROCEEDINGS{10.4108/eai.6-11-2025.2364304, author={Jonatan Jonatan and Giska Auria and Anita Desiani and Ali Amran and Indri Ramayanti and Irmeilyana Irmeilyana}, title={Comparison of Classification Results of Categorical Boosting and Extreme Gradient Boosting Methods in Obesity Class Diagnosis}, proceedings={Proceedings of the 4th Sriwijaya International Conference on Basic and Applied Sciences, SICBAS 2025, 6 November 2025, Palembang, Indonesia}, publisher={EAI}, proceedings_a={SICBAS}, year={2026}, month={8}, keywords={Boosting algorithm CatBoost classification machine learning XGBoost}, doi={10.4108/eai.6-11-2025.2364304} }- Jonatan Jonatan
Giska Auria
Anita Desiani
Ali Amran
Indri Ramayanti
Irmeilyana Irmeilyana
Year: 2026
Comparison of Classification Results of Categorical Boosting and Extreme Gradient Boosting Methods in Obesity Class Diagnosis
SICBAS
EAI
DOI: 10.4108/eai.6-11-2025.2364304
Abstract
Obesity, caused by a chronic energy imbalance in which calorie intake exceeds expenditure, is a growing global health problem that negatively impacts quality of life. Early detection is crucial for mitigation. This study compares the performance of the Extreme Gradient Boosting (XGBoost) and Categorical Boosting (CatBoost) algorithms for obesity status classification. The research methodology included data collection, pre-processing, and model evaluation using two validation strategies: percentage split and k-fold cross-validation. The results show the superiority of CatBoost. In the percentage split evaluation, CatBoost achieved an accuracy of 92.53%, which outperformed XGBoost at 91.67%. Similarly, using K-Fold Cross-Validation, CatBoost yielded an accuracy of 91.95% while XGBoost reached 90.46%. Additionally, CatBoost consistently produced superior average values for recall, F1-score, and precision. It is concluded that CatBoost provides a more accurate and robust model for obesity classification compared to XGBoost. Future research could explore feature engineering techniques or other algorithms to further enhance predictive accuracy.
