CVIA Journal

Announcing a new article publication for Cardiovascular Innovations and Applications journal. Heart disease remains a leading cause of global mortality; consequently, accurate, reliable, and interpretable predictive models are needed for early diagnosis. This study was aimed at developing a robust hybrid ensemble learning framework that improves heart disease predictive accuracy while preserving clinical interpretability.

The Cardio-Sense Ensemble Framework (CSEF) for heart disease prediction was developed by using the publicly available Behavioral Risk Factor Surveillance System dataset. Laplacian binary optimization was used for optimized feature selection, to eliminate redundancy and enhance discriminative information. The refined feature set was used to train multiple baseline classifiers, including logistic regression, random forest, (more…)