Citation

Khan Ghauri, Abdur Rehman and Danish, Muhammad and Nadeem, Lubna and Amin, Yasar and Mahmood, Tariq and Sheraz, Muhammad and Chuah, Teong Chee and Roslee, Mardeni (2024) Detection of Chronic Kidney Disease Using Machine Learning. In: 2024 Multimedia University Engineering Conference (MECON), 23-25 July 2024, Cyberjaya, Malaysia.

Text Detection of Chronic Kidney Disease Using Machine Learning.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

One of the world's worst diseases are chronic kidney disease (CKD), Diabetes and Hypertension which affects millions of people. The development of CKD towards end-stage renal disease can be halted by early identification and prompt treatment. The purpose of this research is to create and assess a machine-learning model for detecting CKD among Diabetes and Hypertension patients. Accuracy of 6 models is improved to their highest, using a dataset of medical record of people already having diabetes and hypertension. Random Forest, Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Gaussian Native Buyer (GNB) and XG Boost models are employed in this work. The k-neighbor, random forest, logistic regression, Support vector machine, GNB models performed at 96.30%, 98.46%, 98.21%, 98.13, 92.78 and XG Boost with highest accuracy of 98.88%. It is evident that the machine learning models which can be beneficial in the clinical diagnosis of CKD are the random forest and logistic regression models. The model's high reliability will aid in the early detection of CKD, allowing for more effective treatment. However, larger and more varied datasets are needed to further validate the model and investigate its potential therapeutic applicability

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Machine learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	07 Feb 2025 00:34
Last Modified:	07 Feb 2025 00:34
URII:	http://shdl.mmu.edu.my/id/eprint/13383

Downloads

Edit (login required)