Binary Particle Swarm Optimization with RAF Based Feature selection in Convolutional Network for Cardiovascular Disease Classification

Accurate prediction of cardiovascular disease (CVD) is essential for timely intervention and improved patient outcomes. This paper presents a hybrid model, BPSO-RAF-CNN that integrates Binary Particle Swarm Optimization (BPSO) with a Regularized Accuracy-Based Fitness Function (RAF) and a Convolutional Neural Network (CNN) to improve prediction performance through optimized feature selection. The approach begins with feature engineering on cardiovascular data, followed by BPSO-RAF to identify the most important, predictively salient and compact feature subset, lowering dimensionality and improving generalization. These selected features are then fed into a CNN for final classification. Extensive experiments demonstrate that BPSO-RAF-CNN outperforms traditional classifiers (Logistic Regression, SVM, Naive Bayes, Decision Tree, Random Forest) achieving an accuracy of 87.05%, Precision 89.71%, Recall 83.77%, F1-score of 86.05%. And Specificity 90.22%, all with a standard deviation 0.5%. The model also shows good performance across 10-fold cross-validation, indicating strong generalization.