Heart Disease Forecast: A Comparative Analysis of Recurrent Neural Network and Long Short Term Memory

Currently, the healthcare industry faces numerous significant challenges, being the need to improve the prognosis accuracy for cardiac illnesses. Using Long Short-Term Memory technique during this test to contribute to the research and development of an intelligent system that can predict cardiac illness. This paper presents a comparison of the RNN and LSTM techniques for forecasting cardiac disease. This comparison prioritizes accuracy and simultaneously considers other prognostic factors. After analysis this research concludes that LSTM algorithm is the most preferable method when compared to the RNN methodology.