Advancing CVD Risk Prediction with Transformer Architectures and Statistical Risk Factor Filtering

Cardiovascular disease (CVD) remains one of the leading causes of mortality worldwide, demanding accurate and timely prediction methods. Recent advancements in artificial intelligence have shown promise in enhancing clinical decision-making for CVD diagnosis. However, many existing models fail to distinguish between statistically significant and redundant risk factors, resulting in reduced interpretability and potential overfitting. This research addresses the need for a clinically meaningful and computationally efficient prediction model. The study utilizes three real-world datasets comprising demographic, clinical, and lifestyle-based risk factors relevant to CVD. A novel methodology is proposed, integrating the HEART framework for statistical feature optimization with a Transformer-based deep learning model for classification. The HEART framework employs correlation-based filtering, Akaike information criterion (AIC), and statistical significance testing to refine feature subsets. The novelty lies in combining statistical risk factor filtration with attention-driven learning, enhancing both model performance and interpretability. The proposed model is evaluated using key metrics, including accuracy, precision, recall, F1-score, AUC, and Jaccard index. Experimental results show that the Transformer model significantly outperforms baseline models, achieving 93.1% accuracy and 0.957 AUC, confirming its potential for reliable CVD prediction.