Predicting hypertension and identifying most important factors among married women in Bangladesh using machine learning approach

Novel Chandra Das, Probir Kumar Ghosh, Md Alamgir Hossain, Uddip Acharjee Shuvo, Nipa Rani Talukder, Fatema Khatun and Mohammad Ziaul Islam Chowdhury

PLOS ONE, 2025, vol. 20, issue 10, 1-34

Abstract: Introduction: Hypertension is a leading contributor to maternal and cardiometabolic morbidity in Bangladesh. We developed and interpreted machine-learning (ML) models to predict hypertension and rank associated factors among married women with the goal of informing targeted screening and policy in low-resource settings. Methods: We analyzed 4,253 married women from the nationally representative BDHS 2017–18 survey (hypertension prevalence: 23.1%). Twelve ML algorithms were trained under six class-balancing strategies with hyperparameters tuned via random search. Validation used a hold-out test set (80/20) and repeated stratified k-fold cross-validation; bootstrap confidence intervals were estimated for the selected model. Model performance was compared with parametric and non-parametric tests. To interpret results, SHAP was used to rank the top 20 predictors and visualize feature effects. Models quantify associations rather than causation. Results: The Extra Trees classifier with SMOTE+ENN achieved the best discrimination (F1 = 0.94; AUC-PR = 0.95; ROC-AUC = 0.95). Compared with the original imbalanced training, minority-class detection improved substantially (Extra Trees F1 increased from 0.08 to 0.94; recall from 0.04 to 0.95) while accuracy and ROC-AUC remained relatively stable across samplers. Statistical testing favored SMOTE+ENN for recall, F1, G-mean and AUC-PR. SHAP identified age, parity, recent births, contraceptive use, spousal education and BMI as key predictors. Younger age (

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0335442

DOI: 10.1371/journal.pone.0335442

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