Comparative performance of urine and blood-based attributes in chronic kidney disease prediction using artificial neural network / M. H. A. Ghafar ... [et al.]

Ghafar, M. H. A. and A. Razak, Abdul Hadi and Megat Ali, Megat Syahirul Amin and Al Junid, Syed Abdul Mutalib and Ahmad, Adizul and A. Latip, Mohd Fuad and Taib, Mohd Nasir and M., Fatimah (2020) Comparative performance of urine and blood-based attributes in chronic kidney disease prediction using artificial neural network / M. H. A. Ghafar ... [et al.]. Journal of Electrical and Electronic Systems Research (JEESR), 17. pp. 17-28. ISSN 1985-5389

Abstract

Chronic Kidney Disease is one of the leading causes of death worldwide. An intelligent diagnostic system capable of evarly detection is therefore, becoming increasingly important. These would allow effective intervention to be delivered to patients, thus prolonging kidney function and reducing risk of mortality. The system should be non-invasive, convenient, accurate, and reliable in detecting the required attributes. This study compares between urine- and blood-based attributes in acute renal failure prediction using artificial neural network. A total of 400 sample data is obtained from UCI Machine Learning Repository. Multiple imputation is then implemented to generate synthetic data. These overcome the issue of missing datapoints and unbalanced sample distribution. Two artificial neural network models are trained. One using the urine-based attributes and the other, using bloodbased attributes. Both models attained excellent classification accuracies of 96.0% and 98.0%, respectively. However, the ANN model developed based on urine-based attributes are recommended intelligent diagnostic systems due to lower computational requirements and the sample acquisition protocol is much convenient for patients and medical practitioners.

Metadata

Item Type: Article
Creators:
Creators
Email / ID Num.
Ghafar, M. H. A.
UNSPECIFIED
A. Razak, Abdul Hadi
hadi@ieee.org
Megat Ali, Megat Syahirul Amin
UNSPECIFIED
Al Junid, Syed Abdul Mutalib
UNSPECIFIED
Ahmad, Adizul
UNSPECIFIED
A. Latip, Mohd Fuad
UNSPECIFIED
Taib, Mohd Nasir
UNSPECIFIED
M., Fatimah
UNSPECIFIED
Subjects: R Medicine > R Medicine (General) > Biomedical engineering
R Medicine > R Medicine (General) > Computer applications to medicine. Medical informatics
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Electrical Engineering
Journal or Publication Title: Journal of Electrical and Electronic Systems Research (JEESR)
UiTM Journal Collections: UiTM Journal > Journal of Electrical and Electronic Systems Research (JEESR)
ISSN: 1985-5389
Volume: 17
Page Range: pp. 17-28
Keywords: Acute renal failure, Blood, Urine, Multiple imputation, Artificial neural network
Date: 2020
URI: https://ir.uitm.edu.my/id/eprint/42380
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