Classifying Patient General Health Using Elman Recurrent Neural Network Method

DOI

10.2991/978-94-6463-413-6_8How to use a DOI?

Keywords

Patient General Health Classification; Elman Recurrent Neural Network (ERNN); Health Prediction

Abstract

This research aims to improve the accuracy and efficiency of patient health classification using the Elman Recurrent Neural Network (ERNN) method. This research utilizes medical data such as blood pressure, heart rate, body temperature, blood sugar levels, cholesterol levels, oxygen levels, and uric acid as parameters in the classification model. The model is trained to classify health categories into “Low,” “Medium,” “High,” and “Very High” disease risk. The data classification results show that the training and testing data achieved an accuracy of 98.49%.

The ERNN method has the potential to help health professionals diagnose diseases more accurately and quickly, thereby allowing patients to receive appropriate and specific treatment for their condition. The research results show the potential of the ERNN method in improving the accuracy and efficiency of patient health classification. However, further research is needed to enhance the model’s generalizability and validate its effectiveness in real-world healthcare settings. This research provides a promising approach to improve patient health classification using the ERNN method. The findings of this study have significant implications for the healthcare sector, as accurate and efficient diagnosis of disease is essential for timely and appropriate treatment.

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  - Ni Luh Gede Candra Leswari
AU  - Putu Sugiartawan
AU  - Gede Dana Paramitha
PY  - 2024
DA  - 2024/05/13
TI  - Classifying Patient General Health Using Elman Recurrent Neural Network Method
BT  - Proceedings of the First International Conference on Applied Mathematics, Statistics, and Computing (ICAMSAC 2023)
PB  - Atlantis Press
SP  - 75
EP  - 85
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-413-6_8
DO  - 10.2991/978-94-6463-413-6_8
ID  - Leswari2024
ER  -