Evaluasi Robustness dan Deployment Readiness Model XGBoost untuk Prediksi Risiko Gagal Jantung di Indonesia

Triandes Sinaga (Universitas Pelita Harapan)
Ayumi Ayumi (Universitas Pelita Harapan)
Jefri Junifer Pangaribuan (Universitas Bina Nusantara)

Jurnal Sains dan Teknologi
Vol. 4 No. 6 (2025): Desember 2025

Cardiovascular diseases, particularly heart failure, remain a leading cause of mortality in Indonesia, affecting an estimated 2.78 million individuals. This study aims to develop a heart failure risk prediction model using the XGBoost algorithm and to evaluate its performance through a comparative validation approach across two datasets with distinct characteristics. The primary model was trained on a large-scale Indonesian population dataset (N = 158,355; 28 features) representing the complexity of real-world clinical data, while the UCI Heart Disease dataset (N = 918; 12 features) was used as a benchmark under more controlled conditions. Experimental results show that the Indonesian model achieved a testing accuracy of 73.50% with a very small training–testing performance gap of 0.53% and an AUC-ROC value of 0.814, indicating strong stability and generalization capability. In contrast, the model trained on the UCI dataset obtained a higher accuracy of 88.59% but exhibited moderate overfitting, reflected by a larger performance gap of 4.60%. Feature importance analysis consistently identified a history of heart disease, hypertension, and smoking behavior as the most influential predictors across both datasets. These findings highlight that model stability and generalization on real-world data are more critical than raw accuracy derived from small, idealized datasets when assessing the clinical deployment readiness of medical artificial intelligence systems in Indonesia.