Analisis Komparasi Algoritma XGBoost dan Logistic Regression Berbasis Explainable AI (SHAP) untuk Deteksi Hipertensi - Risky Radison Nasution; Kurniabudi; Dodo Zaenal Abidin (Universitas Budi Darma, 2026)
Hypertension represents a significant global health challenge due to its frequently asymptomatic nature until serious complications arise. This study aims to conduct an in-depth comparison between XGBoost and Logistic Regression algorithms for the early detection of hypertension, with a specific focus on explainability utilizing the SHAP Permutation Explainer method. The primary issue addressed is the tendency of previous research to manipulate datasets through oversampling techniques, which risks altering the natural distribution of clinical features, alongside the presence of