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July 15, 2025Open Access

Evaluating Feature Selection Methods and Feature Contributions for Cardiovascular Disease Risk Prediction

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Authors

SASuraiya AkhterJMJohn H. Miller

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Overview

Comparative evaluation of feature selection strategies improves predictive accuracy of cardiovascular disease risk models, highlighting their importance.

Key Points

  • The hypergraph-based feature evaluation method achieved the highest predictive accuracy of 75% for cardiovascular disease risk.
  • XGBoost models were developed using selected features, with age, cholesterol, and diabetes being significant predictors.
  • SHAP analysis provided insights into feature contributions, enhancing the interpretability of the model's predictions.
  • Effective feature selection can significantly refine predictive accuracy, aiding clinicians in risk assessment and preventive care.

Cite This Study

Akhter et al. (2025) studied this question.

synapsesocial.com/papers/689a02b6e6551bb0af8cc549https://doi.org/10.1101/2025.07.12.25331445
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