Enhanced cardiovascular disease prediction: AMWOA-based feature selection and PyramidConvFormer-VAE fusion approach

Cardiovascular disease remains a major global cause of death. To address challenges of high dimensionality and data imbalance in heart disease prediction, this study proposes a novel framework integrating feature optimization and classification. An Adaptive Mutated Walrus Optimization Algorithm (AMWOA) effectively reduces feature dimensions, mitigating overfitting and reducing execution time. For classification, a PyramidConvFormer-Variational Autoencoder (VAE) model integrates CNN and transformer layers to extract local-global patterns. Final classification is performed via fully connected layers with softmax activation. Evaluated on the Cleveland dataset using five-fold cross-validation, the proposed method achieves 98.12% accuracy and 98.91% precision, outperforming existing prediction frameworks.