Integration of Generative AI and Deep Tabular Data Learning Architecture for Heart Attack Prediction

Heart attacks, also known as myocardial infarctions, are currently the top cause of death across all age categories. Through early detection, recent advances in healthcare—particularly in artificial intelligence (AI)—have increased the survival rate of heart attack patients. However, an enormous quantity of high-quality data is necessary for meaningful advancement in AI research for heart attacks. Real-world cardiac attack datasets were used to validate the research findings. A detailed analysis showed that the CTGAN model produced synthetic heart attack data superior to expectations. The TabNet architecture also beat all prior deep-learning and machine-learning classifiers in the classification stage. In conclusion, this study shows the potential of using deep generative models to generate high-quality synthetic data for heart attack research. It emphasizes the superior performance of interpretable TabNet over traditional machine-learning methods in categorizing heart attacks.