Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Limits: Journal of Mathematics and Its Applications
Muhammad Edy Rizal
Universitas Tadulako
Computer Science & IT Mathematics

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Klasifikasi Obesitas Menggunakan Domain-Based Decision Tree, XGBoost, dan SHAP Embay Rohaeti; Ani Andriyati; Muhammad Edy Rizal
Limits: Journal of Mathematics and Its Applications Vol. 23 No. 1 (2026): Limits: Journal of Mathematics and Its Applications Volume 23 Nomor 1 Edisi Ap
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v23i1.7970

Abstract

Obesity is a complex public health issue that requires effective early identification strategies to mitigate its long-term health impacts. This study aimed to classify obesity levels, categorized as Underweight, Normal, Overweight, and Obese, using 14 predictors grouped into three domains: biological, dietary, and physical activity. In addition to developing an accurate predictive model, the study investigated which domain contributes most to obesity classification. Two complementary modeling strategies were applied: domain-specific decision tree models to evaluate the predictive strength of each domain independently, and a comprehensive model using eXtreme Gradient Boosting (XGBoost) trained on all predictors. To address class imbalance, SMOTENC oversampling was applied to the training set, and hyperparameter tuning was performed via cross-validation for both approaches. Evaluation on the test set showed that the XGBoost model outperformed the domain-based decision trees across all performance metrics, including balanced accuracy, precision, recall, specificity, and F1-score. While decision trees offered domain-level interpretability, they lacked the predictive power of the integrated model. SHAP (SHapley Additive Explanations) analysis revealed that influential features spanned all domains, with Age, Vegetable consumption, and Transportation type emerging as top predictors. These findings demonstrate that integrating multi-domain behavioral data enhances both the accuracy and interpretability of obesity classification models, supporting the use of interpretable machine learning for personalized health risk assessment and prevention strategies
Co-Authors Ani Andriyati Embay Rohaeti