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
Cardiovascular risk assessment remains a central challenge in both population-based prevention and high-risk clinical settings. We benchmarked a previously developed mechanistic model of cardiovascular ageing against machine-learning baselines across two distinct cohorts and evaluated both discrimination and probabilit...
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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