Hypertension distribution in the original dataset, and balanced class label distribution after applying the Synthetic Minority Oversampling Technique (SMOTE) for training and test data.

Hypertension distribution in the original dataset, and balanced class label distribution after applying the Synthetic Minority Oversampling Technique (SMOTE) for training and test data.

<p>Hypertension distribution in the original dataset, and balanced class label distribution after applying the Synthetic Minority Oversampling Technique (SMOTE) for training and test data.</p>

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Bibliographic Details
Main Author:	Probir Kumar Ghosh (6947282) (author)
Other Authors:	Md. Aminul Islam (6750650) (author), Md. Ahshanul Haque (10525794) (author), Md. Tariqujjaman (7038959) (author), Novel Chandra Das (19742953) (author), Mohammad Ali (73095) (author), Md. Rasel Uddin (21648057) (author), Md. Golam Dostogir Harun (7208396) (author)
Published:	2025
Subjects:	Medicine Biotechnology Computational Biology Mathematical Sciences not elsewhere classified systolic blood pressure shapley additive explanations refined risk estimate random forest model individual conditional expectation estimating causal relationships double machine learning diastolic blood pressure assessing causal effect 90 &# 8201 79 &# 8211 140 &# 8201 07 &# 8211 body mass index used logistic regression nonlinear observed confounders dataset included 11 biased methods provided 17 &# 8211 excessive body weight shap plots reveal identified older age health surveys data demonstrating complex interactions ses ), self body weight used 11 ses ), 17 ), nonlinear relation health population biased method dml ), hypertension data xlink "> targeted interventions study aims socioeconomic status significantly contributed shown effectiveness mm hg limited settings key predictors income countries including age identifying predictors identify predictors female gender evaluation f1 average age
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