Data selection flow chart.

<div><p>Introduction</p><p>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 informi...

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Bibliographic Details
Main Author:	Novel Chandra Das (19742953) (author)
Other Authors:	Probir Kumar Ghosh (6947282) (author), Md. Alamgir Hossain (1371456) (author), Uddip Acharjee Shuvo (22530303) (author), Nipa Rani Talukder (22530306) (author), Fatema Khatun (637493) (author), Mohammad Ziaul Islam Chowdhury (12369350) (author)
Published:	2025
Subjects:	Medicine Biotechnology Infectious Diseases Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified visualize feature effects repeated stratified k original imbalanced training limited biometric markers informing targeted screening husbands &# 8217 extra trees classifier bootstrap confidence intervals behavioral variables supplemented 253 married women 2 &# 8211 twelve ml algorithms shap identified age parity &# 8805 top 20 predictors xlink "> hypertension xlink "> key predictors predicting hypertension predict hypertension hypertension prevalence underweight status test set spousal education sms interventions six class selected model risk scores risk regions recent births postpartum visits parity households model performance leading contributor interpreted machine interpret results fold cross focused education family planning contraceptive use cardiometabolic morbidity best discrimination balancing strategies analyzed 4 40 years 35 years 18 survey 1 %).
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Data selection flow chart.

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