Prediction of Personalised Hypertension Using Machine Learning in Indonesian Population.

This study aims to enhance individual hypertension risk prediction in Indonesia using machine learning (ML) models. The research investigates the predictive accuracy of models with and without incorporating personal hypertension history, seeking to understand how data limitations impact model performance in a low-resource setting. Data from the SATUSEHAT IndonesiaKu (ASIK) system were preprocessed and filtered to create a dataset of 9.58 million adult health records. Two primary model variations were compared: Model A (incorporating patient history) and Model B (excluding patient history). We evaluated the model using five algorithms: XGBoost, LightGBM, CatBoost, Logistic Regression, and Random Forest. Model performance was assessed using the Area Under the Curve (AUC), sensitivity, and specificity metrics. Model A achieved superior predictive accuracy (AUC = 0.85) compared to Model B (AUC = 0.78). To mitigate potential bias, Model B was selected for further in-depth development. Evaluation of model B reveals that XGBoost and LightGBM algorithm achieved the highest performance (AUC 0.78) and LightGBM emerged as the best algorithm based on its performance. SHAP analysis was conducted and identified key predictors such as age, family history of hypertension, body weight, and waist circumference. This study finds that while a patient's personal history of hypertension significantly enhances predictive accuracy, robust ML models can effectively predict hypertension risk using other accessible demographic, clinical, and lifestyle features. Model B offers a valuable and generalizable approach for broader risk screening, particularly where patient history may be unavailable or unreliable, while also providing insights into key modifiable and non-modifiable determinants of hypertension.