Results show that computationally efficient and interpretable ensembles provide a viable alternative to more complex models, particularly in data-constrained clinical settings, and highlight the potential of interpretable ensemble learning to develop dependable, deployable clinical decision support tools for integration into hospital or e-health systems for early CVD risk stratification.
Background Accurate primary-prevention risk stratification for coronary artery disease (CAD) remains challenging. We developed and internally validated a framework integrating clinical variables with a polygenic risk score (PRS). Methods This three-center retrospective cohort included 2,067 adults without baseline CAD....
F. Lei, Xiang-Wen Xi, Zi-He Dang et al.· Frontiers in Cardiovascular...· 0 citations
Overall, cohort-specific machine-learning models demonstrated stronger discrimination, while the external applicability of the mechanistic framework depended on the alignment of available predictors and clinical endpoints in the target cohort.
M. Suleimenova, R. Amanova, Arailym Keneskanova· BioMedInformatics· 0 citations
Under a leakage-controlled, unbiased evaluation, XGBoost provided moderate but trustworthy discrimination together with well-calibrated probabilities for diabetes prediction, while SHAP confirmed clinically plausible predictors.
Z. Kucukakcali, I. Cicek· International Journal of Med...· 0 citations
It is demonstrated that integrating feature selection, ensemble learning, and threshold calibration can improve heart disease detection while maintaining predictive stability, providing a clinically aligned machine learning framework for cardiovascular risk assessment.
Mehdi Mostofi, T. Banirostam· International Journal of Ele...· 0 citations
This study developed an AutoML-based CVD risk prediction model with superior discrimination and calibration, providing clinicians a practical tool for risk stratification, and has the potential to reduce CVD burden at the population level.
Si-Min He, Ju-Ping Wang, Le Zhao et al.· International Journal of Car...· 0 citations
Findings highlight blood pressure and body size measures as the dominant clinical signals in this dataset, while demonstrating the potential of an explainable machine-learning model based on routinely collected clinical data to support cardiovascular risk stratification and clinical decision-making in hypertensive pati...
C. M. Anyanwu, J. C. Onyianta, Ogechi Gift Onyedi et al.· Nature Journal of Emerging S...· 0 citations
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