Clinically Translatable Uncertainty Aware Machine Learning for Cardiovascular Disease Risk Analysis
- 1Arizona State University
Abstract
ImportanceCardiovascular disease remains the leading cause of mortality worldwide. Although machine learning models have demonstrated strong predictive performance, limited attention has been given to uncertainty quantification and gender-specific risk factor identification, which are essential for clinically trustworthy artificial intelligence.
ObjectiveTo develop and evaluate an interpretable and uncertainty-aware machine learning framework for cardiovascular disease prediction that integrates an association study on biomarkers, explainable artificial intelligence, and gender-stratified risk assessment.
Design, Setting, and ParticipantsThis retrospective observational study utilized the UCI Heart Disease dataset comprising 918 participants, including 725 males and 193 females. Demographic, clinical, laboratory, and exercise-related variables were analyzed. Missing values were addressed using a hybrid imputation strategy combining MICE and supervised Random Forest imputation.
MethodsAn ensemble machine learning imputation strategy was developed to address missingness. Then advanced machine learning models were developed and evaluated using stratified training/testing partitions and 5-fold cross-validation. Predictive performance was assessed using several evaluation metrics. Uncertainty was quantified through bootstrap resampling, predictive entropy, and mutual information. Linear Double machine learning and Causal Forest double machine learning were applied to estimate gender-specific averages and association studies for risk factors.
ResultsRandom Forest achieved the strongest and most stable predictive performance across male and female cohorts, with ROC-AUC values exceeding 0.90 and robust cross-validation performance. Hybrid imputation improved classification performance, increasing the F1-score from 0.87 to 0.89 and ROC-AUC from 0.90 to 0.92. The causal association study analyzes identified the number of major vessels, ST-segment slope, thalassemia, and exercise-induced angina as the strongest determinants of heart disease, with notable differences observed between male and female subgroups.
Conclusions and RelevanceIntegrating predictive modeling, uncertainty quantification, and association study machine learning provides a robust and clinically interpretable framework for cardiovascular disease risk assessment. The proposed approach improves prediction reliability while identifying gender-specific causally important risk factors, supporting precision cardiovascular medicine and trustworthy clinical artificial intelligence.
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BibTeX
@misc{roy2026clinically,
title = {{Clinically Translatable Uncertainty Aware Machine Learning for Cardiovascular Disease Risk Analysis}},
author = {S. Roy and R. Imran and M. R. Haque and F. Mostafa},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.18.26363375},
url = {https://www.medrxiv.org/content/10.64898/2026.09.18.26363375v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Clinically Translatable Uncertainty Aware Machine Learning for Cardiovascular Disease Risk Analysis
AU - S. Roy
AU - R. Imran
AU - M. R. Haque
AU - F. Mostafa
PY - 2026
DA - 2026/09/18
PB - medRxiv
DO - 10.64898/2026.09.18.26363375
UR - https://www.medrxiv.org/content/10.64898/2026.09.18.26363375v1
AB - ImportanceCardiovascular disease remains the leading cause of mortality worldwide. Although machine learning models have demonstrated strong predictive performance, limited attention has been given to uncertainty quantification and gender-specific risk factor identification, which are essential for clinically trustworthy artificial intelligence. ObjectiveTo develop and evaluate an interpretable and uncertainty-aware machine learning framework for cardiovascular disease prediction that integrates an association study on biomarkers, explainable artificial intelligence, and gender-stratified risk assessment. Design, Setting, and ParticipantsThis retrospective observational study utilized the UCI Heart Disease dataset comprising 918 participants, including 725 males and 193 females. Demographic, clinical, laboratory, and exercise-related variables were analyzed. Missing values were addressed using a hybrid imputation strategy combining MICE and supervised Random Forest imputation. MethodsAn ensemble machine learning imputation strategy was developed to address missingness. Then advanced machine learning models were developed and evaluated using stratified training/testing partitions and 5-fold cross-validation. Predictive performance was assessed using several evaluation metrics. Uncertainty was quantified through bootstrap resampling, predictive entropy, and mutual information. Linear Double machine learning and Causal Forest double machine learning were applied to estimate gender-specific averages and association studies for risk factors. ResultsRandom Forest achieved the strongest and most stable predictive performance across male and female cohorts, with ROC-AUC values exceeding 0.90 and robust cross-validation performance. Hybrid imputation improved classification performance, increasing the F1-score from 0.87 to 0.89 and ROC-AUC from 0.90 to 0.92. The causal association study analyzes identified the number of major vessels, ST-segment slope, thalassemia, and exercise-induced angina as the strongest determinants of heart disease, with notable differences observed between male and female subgroups. Conclusions and RelevanceIntegrating predictive modeling, uncertainty quantification, and association study machine learning provides a robust and clinically interpretable framework for cardiovascular disease risk assessment. The proposed approach improves prediction reliability while identifying gender-specific causally important risk factors, supporting precision cardiovascular medicine and trustworthy clinical artificial intelligence.
ER -