Optimizing Cardiovascular Risk Prediction using Explainable Machine Learning and Clinical Patient Data
Abstract
Cardiovascular disease (CVD) remains one of the leading causes of morbidity and mortality worldwide, creating a continuing need for accurate, timely, and clinically useful risk prediction. Conventional cardiovascular risk assessment methods, including logistic regression-based scoring systems, provide valuable estimates but may have limitations when handling complex nonlinear relationships, interactions among clinical variables, and high-dimensional patient data. Machine learning (ML) offers opportunities to improve predictive performance by identifying hidden patterns within routinely collected clinical information. However, the clinical adoption of ML-based prediction is constrained by concerns regarding transparency, interpretability, fairness, and trust. This study proposes an explainable machine learning framework for optimizing cardiovascular risk prediction using clinical patient data. The methodology incorporates data preprocessing, feature selection, multiple ML algorithms, hyperparameter optimization, model validation, and explainability techniques such as SHAP and LIME. Clinical variables including age, sex, blood pressure, cholesterol, glucose, body mass index, smoking status, diabetes, and other relevant patient characteristics can be evaluated to determine their contribution to cardiovascular risk. Model performance is assessed using discrimination, calibration, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic curve. Explainable outputs are used to identify important predictors and provide patient-level reasoning for model predictions. The proposed approach seeks to balance predictive accuracy with clinical interpretability, supporting more transparent and evidence-informed cardiovascular risk assessment.
Article Information
Journal |
International Journal of Emerging Trends in Engineering and Management Research |
|---|---|
Volume (Issue) |
Vol. 11 No. 2 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) |
DOI |
|
Pages |
19662-19671 |
Published |
March 19, 2026 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Dr Somasundaram Krishnan (%2026). Optimizing Cardiovascular Risk Prediction using Explainable Machine Learning and Clinical Patient Data. International Journal of Emerging Trends in Engineering and Management Research , Vol. 11 No. 2 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 19662-19671. https://doi.org/10.15662/ijetemr.2026.1102011 |
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