Abstract
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.