Identification of Heart Disease in Patients Using the Long Short-Term Memory (LSTM) Method

DOI

10.2991/978-94-6463-413-6_15How to use a DOI?

Keywords

LSTM; Disease Heart; Predicting; Identification

Abstract

Heart disease is a deadly disease that is often the cause of death worldwide. This is a category of cardiovascular disease involving various heart disorders, including coronary artery disease, heart failure, and arrhythmia. Heart disease can also cause serious complications such as stroke, ruptured blood vessels, and peripheral artery disease. Long Short-Term Memory (LSTM) is one of the developments in ANN that will be designed to overcome a vanishing gradient problem to enable the network to increase information for an extended period in the incoming sequence. LSTM can understand patterns in this data and identify signs of heart disease, such as arrhythmias or abnormal blood pressure fluctuations. Heart disease can also cause serious Health is the most important thing for everyone, environmental factors are one of the main factors influencing a person’s health. Heart disease is a deadly disease that is often the cause of death worldwide. The LSTM method in the book can predict heart disease, and this disease requires very efficient learning because very large architectures can be trained successfully. Heart disease can also cause serious. The main goal of early identification of heart disease is to detect cardiovascular problems early so that appropriate medical measures and lifestyle changes can be taken to manage the disease. Identification of heart disease can be recognized by reading data patterns using an LSTM method with 83% accuracy.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Made Priska Sylviana Benyamin
AU  - Putu Sugiartawan
AU  - Putu Shinta Noviaty
PY  - 2024
DA  - 2024/05/13
TI  - Identification of Heart Disease in Patients Using the Long Short-Term Memory (LSTM) Method
BT  - Proceedings of the First International Conference on Applied Mathematics, Statistics, and Computing (ICAMSAC 2023)
PB  - Atlantis Press
SP  - 149
EP  - 159
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-413-6_15
DO  - 10.2991/978-94-6463-413-6_15
ID  - Benyamin2024
ER  -