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The research investigates the predictive\u00a0accuracy of models with and without incorporating personal hypertension history,\u00a0seeking to understand how data limitations impact model performance in a low-resource setting.\u00a0Data 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.\u00a0Model A achieved superior predictive accuracy (AUC\u2009=\u20090.85) compared to Model B (AUC\u2009=\u20090.78).\u00a0To 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.\u00a0This study finds that while a patient\u2019s 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.<\/jats:p>","DOI":"10.1007\/s10916-025-02253-5","type":"journal-article","created":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T00:28:19Z","timestamp":1760315299000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Prediction of Personalised Hypertension Using Machine Learning in Indonesian Population"],"prefix":"10.1007","volume":"49","author":[{"given":"Edo","family":"Septian","sequence":"first","affiliation":[]},{"given":"Muhammad Rizal","family":"Khaefi","sequence":"additional","affiliation":[]},{"given":"Achmad","family":"Athoillah","sequence":"additional","affiliation":[]},{"given":"Dewi Nur","family":"Aisyah","sequence":"additional","affiliation":[]},{"given":"Muhammad","family":"Hardhantyo","sequence":"additional","affiliation":[]},{"given":"Fauziah Mauly","family":"Rahman","sequence":"additional","affiliation":[]},{"given":"Logan","family":"Manikam","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,13]]},"reference":[{"issue":"04","key":"2253_CR1","doi-asserted-by":"publisher","first-page":"e12927","DOI":"10.22146\/bkm.v40i04.12927","volume":"04","author":"H Djasri","year":"2024","unstructured":"Djasri H, Alfajri NZ, Asdary RN, Wardoyo MHP. 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The data in this study is\u00a0already de-identified and aggregated reports without any direct patient\u00a0identifiers, ensuring the confidentiality and privacy of individuals.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"137"}}