Citation

Al-Amoodi, W. and Supriyanto, E. and Warid, M. N. M. (2019) Chronic Kidney Disease Risk Estimation Using Artificial Neural Network. International Journal on Robotics, Automation and Sciences, 1. pp. 4-13. ISSN 2682-860X

Text View of Chronic Kidney Disease Risk Estimation Using Artificial Neural Network.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

Chronic kidney disease (CKD) is the most common disease of the urinarysystem that can threaten the survival of the humanbody. Early detection and lifestyle changes can prevent kidney failure and improve the chanceof survival. In West Malaysia, the prevalence of chronic kidney disease is estimated to be 9% of the population. However, screening for chronic kidney disease is still neglected at the early stages. Many equations for risk estimation of kidney failure have been developed. Some of the limitations of these equations are that they may require many laboratory tests, static and not updated. In this study, a new risk estimation model for kidney disease is developed. The risk factors of kidney disease are first identified according to their energy levels, which are Low, Medium and High. The new equation is then developed based on the relationship and the estimated weight of these risk factors. Artificial Neural Network (ANN) is utilized in this study as an alternative to classic risk equations. The MATLAB software is used to train the neural network. Retrospective data from 20 subjects are used to compare the output for the conventional equation and ANN. Another 20 samples have also been generated and compared with “Kidney Disease: Improving Global Outcomes” (KDIGO) 2012 clinical guideline heat map. The results show a slightdifference between the methods. The conventional method shows its capability to estimate the risk. The result also shows the potential of the artificial neural network (ANN) to improve the accuracy of chronic kidney disease risk estimation.

Item Type:	Article
Uncontrolled Keywords:	RiskEstimation, Artificial Neural Network
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science R Medicine > RC Internal medicine
Divisions:	Others
Depositing User:	Mr. MUHAMMAD AZRUL MOSRI
Date Deposited:	15 Jul 2024 02:33
Last Modified:	15 Jul 2024 02:33
URII:	http://shdl.mmu.edu.my/id/eprint/12609

Downloads

Edit (login required)