Explainable machine learning for risk prediction of reduced quality of life in hypertension
Author(s)
Type
Journal Article
Abstract
Background
Individuals with hypertension are at risk to reduced quality of life (QoL). Explainable machine learning (ML) can be used for domain-specific risk stratification and prioritization of modifiable determinants of low QoL.
Objective
To train ML classifier algorithms for QoL risk stratification in hypertension, where meaningful determinants were explored through Shapley additive explanations (SHAP).
Methods
Data from hypertensive individuals (n = 534) completed WHOQOL BREF, Quick Physical Activity Rating, Morisky Medication Adherence Scale 8, and standardized questionnaires for acceptance and knowledge were analyzed utilizing Decision Tree, Gradient Boosting, XGBoost, AdaBoost, Random Forest, and Naive Bayes ML classifiers. The trained ML algorithms were evaluated using stratified 10-fold cross-validation, where the stability was examined using rank-based metrics. SHAP were applied to the gradient boosting, as the most stable model.
Results
For physical QoL, Random Forest (AUC 0.850; sensitivity 0.835; specificity 0.738) and Gradient Boosting (AUC 0.850; sensitivity 0.801; specificity 0.764) showed good reduced QoL identification. For psychological domain, best classifications were obtained from Gradient Boosting performed best (AUC 0.833; sensitivity 0.818; specificity 0.651) and XGBoost (AUC 0.831; sensitivity 0.824; specificity 0.660), with the former observed as the most stable SHAP analysis identified acceptance and medication adherence as the dominant shared drivers of risk across both QoL domains. Physical QoL risk was further influenced by physical activity–related factors, whereas Psychological QoL risk showed additional contributions from age and educational attainment.
Conclusion
Ensemble tree–based classifiers, particularly Gradient Boosting, had the most optimal performance in discriminating reduced and good QoL. Acceptance and medication adherence are the most influential shared drivers of risk, while physical activity, age, and educational attainment contributed to domain-specific heterogeneity.
Individuals with hypertension are at risk to reduced quality of life (QoL). Explainable machine learning (ML) can be used for domain-specific risk stratification and prioritization of modifiable determinants of low QoL.
Objective
To train ML classifier algorithms for QoL risk stratification in hypertension, where meaningful determinants were explored through Shapley additive explanations (SHAP).
Methods
Data from hypertensive individuals (n = 534) completed WHOQOL BREF, Quick Physical Activity Rating, Morisky Medication Adherence Scale 8, and standardized questionnaires for acceptance and knowledge were analyzed utilizing Decision Tree, Gradient Boosting, XGBoost, AdaBoost, Random Forest, and Naive Bayes ML classifiers. The trained ML algorithms were evaluated using stratified 10-fold cross-validation, where the stability was examined using rank-based metrics. SHAP were applied to the gradient boosting, as the most stable model.
Results
For physical QoL, Random Forest (AUC 0.850; sensitivity 0.835; specificity 0.738) and Gradient Boosting (AUC 0.850; sensitivity 0.801; specificity 0.764) showed good reduced QoL identification. For psychological domain, best classifications were obtained from Gradient Boosting performed best (AUC 0.833; sensitivity 0.818; specificity 0.651) and XGBoost (AUC 0.831; sensitivity 0.824; specificity 0.660), with the former observed as the most stable SHAP analysis identified acceptance and medication adherence as the dominant shared drivers of risk across both QoL domains. Physical QoL risk was further influenced by physical activity–related factors, whereas Psychological QoL risk showed additional contributions from age and educational attainment.
Conclusion
Ensemble tree–based classifiers, particularly Gradient Boosting, had the most optimal performance in discriminating reduced and good QoL. Acceptance and medication adherence are the most influential shared drivers of risk, while physical activity, age, and educational attainment contributed to domain-specific heterogeneity.
Date Issued
2026-12-01
Date Acceptance
2026-07-01
Citation
Vascular Health and Risk Management, 2026, 22
ISSN
1176-6344
Publisher
Dove Medical Press
Journal / Book Title
Vascular Health and Risk Management
Volume
22
Copyright Statement
© 2026 Andala et al. This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v4.0) License (http://creativecommons.org/licenses/by-nc/4.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
License URL
Publication Status
Published
Article Number
614061
Date Publish Online
2026-07-13