Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study

Njoud Abdullah Almansour; Hajra Fahim Syed; Nuha Radwan Khayat; Rawan Kanaan Altheeb; Renad Emad Juri; Jamal Alhiyafi; Saleh Alrashed; Sunday O Olatunji

doi:10.1016/j.compbiomed.2019.04.017

Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study

Comput Biol Med. 2019 Jun:109:101-111. doi: 10.1016/j.compbiomed.2019.04.017. Epub 2019 Apr 25.

Authors

Njoud Abdullah Almansour¹, Hajra Fahim Syed², Nuha Radwan Khayat³, Rawan Kanaan Altheeb⁴, Renad Emad Juri⁵, Jamal Alhiyafi⁶, Saleh Alrashed⁷, Sunday O Olatunji⁸

Affiliations

¹ Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, PO Box 1982, Dammam, Saudi Arabia. Electronic address: 2130008151@iau.edu.sa.
² Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, PO Box 1982, Dammam, Saudi Arabia. Electronic address: 2130009210@iau.edu.sa.
³ Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, PO Box 1982, Dammam, Saudi Arabia. Electronic address: 2130005966@iau.edu.sa.
⁴ Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, PO Box 1982, Dammam, Saudi Arabia. Electronic address: 2130007114@iau.edu.sa.
⁵ Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, PO Box 1982, Dammam, Saudi Arabia. Electronic address: 2130009362@iau.edu.sa.
⁶ Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, PO Box 1982, Dammam, Saudi Arabia. Electronic address: jalhiyafi@iau.edu.sa.
⁷ Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, PO Box 1982, Dammam, Saudi Arabia. Electronic address: saalrashed@iau.edu.sa.
⁸ Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, PO Box 1982, Dammam, Saudi Arabia. Electronic address: osunday@iau.edu.sa.

PMID: 31054385
DOI: 10.1016/j.compbiomed.2019.04.017

Abstract

This paper aims to assist in the prevention of Chronic Kidney Disease (CKD) by utilizing machine learning techniques to diagnose CKD at an early stage. Kidney diseases are disorders that disrupt the normal function of the kidney. As the percentage of patients affected by CKD is significantly increasing, effective prediction procedures should be considered. In this paper, we focus on applying different machine learning classification algorithms to a dataset of 400 patients and 24 attributes related to diagnosis of chronic kidney disease. The classification techniques used in this study include Artificial Neural Network (ANN) and Support Vector Machine (SVM). To perform experiments, all missing values in the dataset were replaced by the mean of the corresponding attributes. Then, the optimized parameters for the Artificial Neural Network (ANN) and Support Vector Machine (SVM) techniques were determined by tuning the parameters and performing several experiments. The final models of the two proposed techniques were developed using the best-obtained parameters and features. The empirical results from the experiments indicated that ANN performed better than SVM, with accuracies of 99.75% and 97.75%, respectively, indicating that the outcome of this study is very promising.

Keywords: Artificial Neural Network (ANN); Chronic Kidney Disease (CKD); Machine learning; Support Vector Machine (SVM).

MeSH terms

Diagnosis, Computer-Assisted*
Humans
Neural Networks, Computer*
Renal Insufficiency, Chronic / diagnosis*
Support Vector Machine*