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Diagnosis and Classification of Chronic Renal Failure Utilising Intelligent Data Mining Classifiers

Diagnosis and Classification of Chronic Renal Failure Utilising Intelligent Data Mining Classifiers

Abeer Y. Al-Hyari, Ahmad M. Al-Taee, Majid A. Al-Taee
Copyright: © 2014 |Volume: 9 |Issue: 4 |Pages: 12
ISSN: 1554-1045|EISSN: 1554-1053|EISBN13: 9781466655324|DOI: 10.4018/ijitwe.2014100101
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MLA

Al-Hyari, Abeer Y., et al. "Diagnosis and Classification of Chronic Renal Failure Utilising Intelligent Data Mining Classifiers." IJITWE vol.9, no.4 2014: pp.1-12. http://doi.org/10.4018/ijitwe.2014100101

APA

Al-Hyari, A. Y., Al-Taee, A. M., & Al-Taee, M. A. (2014). Diagnosis and Classification of Chronic Renal Failure Utilising Intelligent Data Mining Classifiers. International Journal of Information Technology and Web Engineering (IJITWE), 9(4), 1-12. http://doi.org/10.4018/ijitwe.2014100101

Chicago

Al-Hyari, Abeer Y., Ahmad M. Al-Taee, and Majid A. Al-Taee. "Diagnosis and Classification of Chronic Renal Failure Utilising Intelligent Data Mining Classifiers," International Journal of Information Technology and Web Engineering (IJITWE) 9, no.4: 1-12. http://doi.org/10.4018/ijitwe.2014100101

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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 previously reported CRF diagnosis system which was based on Artificial Neural Network (ANN), Decision Tree (DT) and Naïve Bayes (NB) classifying algorithms. This is achieved by utilizing more efficient data mining classifiers, 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, specificity 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 classifiers. The obtained results showed SVM to be the most accurate (93.14%) when compared to LR as well as other classifiers 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.

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