Abstract : Cardiovascular diseases have long been a significant medical concern, and early and accurate identification is crucial for effective rehabilitation and treatment. Predicting heart disease promptly enables informed decision-making, reducing patient risks. We utilized the MIT-BIH Database to facilitate this process, containing 48 half-hour excerpts of two-channel ambulatory ECG recordings digitized at 360 samples per second per channel. Before further analysis, the data underwent preprocessing ste
Keywords : Deep Learning, Based Heart Attack Prediction Using, Architecture Cardiovascular, deep, learning