Optimasi Prediksi Hipertensi Menggunakan Logistic Regression Berbasis Borderline SMOTE dan Penjelasan Model dengan SHAP

Risky Radison Nasution; Kurniabudi Kurniabudi; Dodo Zaenal Abidin

doi:10.61132/prosemnasproit.v2i2.139

Authors

Risky Radison Nasution Universitas Dinamika Bangsa
Kurniabudi Kurniabudi Universitas Dinamika Bangsa
Dodo Zaenal Abidin Universitas Dinamika Bangsa

DOI:

https://doi.org/10.61132/prosemnasproit.v2i2.139

Keywords:

Borderline-SMOTE, Explainable Artificial Intelligence, Hipertensi, Logistic Regression, SHAP

Abstract

Hypertension is a major global health risk that requires accurate early detection, yet conventional methods struggle with complex and imbalanced health datasets. This study aims to optimize hypertension prediction using a Logistic Regression model integrated with Borderline-SMOTE to enhance recall and provide model transparency through SHAP (Shapley Additive Explanations). The method utilizes the BRFSS dataset, applying Borderline-SMOTE to address class imbalance at the decision boundary and XAI techniques for global and local interpretation. The findings show that the model achieved an accuracy of 0.719, an AUC of 0.800, and a significantly improved recall of 0.756. SHAP analysis identified age, high cholesterol, and BMI as the most influential risk factors, while waterfall plots successfully clarified individual risk extremes, ranging from 1.72% to 99.43% probability. These results imply that the proposed approach provides a sensitive and transparent screening tool for public health practitioners, effectively balancing statistical efficiency with clinical accountability.

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