International Journal of Information Technology and Web Engineering, 9(4), 1-12, October-December 2014 1
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ABSTRACT
This paper presents a new clinical decision support system for diagnosing patients with Chronic Renal Failure
(CRF) which is not yet thoroughly explored in literature. This paper aims at improving performance of a pre-
viously reported CRF diagnosis system which was based on Artifcial Neural Network (ANN), Decision Tree
(DT) and Naïve Bayes (NB) classifying algorithms. This is achieved by utilizing more effcient data mining
classifers, Support Vector Machine (SVM) and Logistic Regression (LR), in order to: (i) diagnose patients
with CRF and (ii) determine the rate at which the disease is progressing. A clinical dataset of more than 100
instances is used in this study. Performance of the developed decision support system is assessed in terms
of diagnostic accuracy, sensitivity, specifcity and decisions made by consultant specialist physicians. The
open source Waikato Environment for Knowledge Analysis library is used in this study to build and evaluate
performance of the developed data mining classifers. The obtained results showed SVM to be the most ac-
curate (93.14%) when compared to LR as well as other classifers reported in the previous study. A complete
system prototype has been developed and tested successfully with the aid of NHS collaborators to support
both diagnosis and long-term management of the disease.
Diagnosis and Classifcation
of Chronic Renal Failure
Utilising Intelligent Data
Mining Classifers
Abeer Y. Al-Hyari, The University of Jordan, Amman, Jordan
Ahmad M. Al-Taee, The University of Jordan, Amman, Jordan
Majid A. Al-Taee, The University of Liverpool, Liverpool, UK
Keywords: Artifcial Neural Networks, Chronic Renal Failure, Clinical Decision Support, Data
Classifcation, Decision Tree, Logistic Regression, Naïve Bayes, Support Vector Machines
1. INTRODUCTION
Chronic Renal Failure (CRF) is defined as
a reduction in function of the kidneys that
develops over months to years (Goldman &
Schafer, 2014). Under normal circumstances,
the kidneys function to remove wastes, main-
tain acid base balance, and control fluid and
electrolytes balance in the body (WebMed,
2014). Therefore, chronic kidney disease can
lead to dangerous metabolic derangements,
electrolyte abnormalities and accumulation
DOI: 10.4018/ijitwe.2014100101