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

Publication Type : Conference Paper

Publisher : IEEE

Source : 2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON)

Url : https://doi.org/10.1109/delcon64804.2024.10866161

Campus : Amaravati

School : School of Computing

Year : 2024

Abstract : 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.

Cite this Research Publication : Bollapalli Althaph, Nagendra Panini Challa, Jajam Nagaraju, Kamepalli S. L. Prasanna, Heart Disease Forecast: A Comparative Analysis of Recurrent Neural Network and Long Short Term Memory, 2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON), IEEE, 2024, https://doi.org/10.1109/delcon64804.2024.10866161

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