Enhancing Heart Disease Prediction Using CardiAI: With Key Performance Metrics Accuracy, Precision, Recall and F1-Score - Mulaguri, Asish Tony; Katta, Sai Krishna; Karanam, Venkata Kousik; Pachipala, Yellamma; Narisety, Sriyaa (International Journal of Intelligent Systems and Applications in Engineering, 2024)
In this study, we introduce CardiAI, a pioneering deep learning algorithm meticulously crafted for precise heart disease prediction. Unlike existing models, CardiAI's innovation lies in its intricate architecture, integrating strategic dropout layers, early stopping mechanisms, and sophisticated techniques to mitigate overfitting. Our extensive comparative analysis showcases CardiAI's exceptional performance, surpassing traditional models such as support vector regression, logistic regression, and k-nearest neighbours. Demonstrating unparalleled accuracy without compromising efficiency, CardiAI achieves remarkable predictive rates, signifying a significant advancement in heart disease diagnostics. This research presents a transformative leap in cardiovascular healthcare, offering a more accurate and efficient predictive model that facilitates early disease detection and informed patient management. The breakthrough potential of CardiAI stands poised to revolutionize medical diagnostics, promising to significantly improve patient outcomes while optimizing healthcare resources.