NDLI: Clinical decision support system for diagnosis and management of Chronic Renal Failure

Content Provider	IEEE Xplore Digital Library
Author	Al-Hyari, A.Y. Al-Taee, A.M. Al-Taee, M.A.
Copyright Year	2013
Description	Author affiliation: Comput. Eng. Dept., Al-Balqa' Appl. Univ., Al-Salt, Jordan (Al-Hyari, A.Y.) \|\| Sch. of Comput. & IS, Kingston Univ., London, UK (Al-Taee, M.A.) \|\| Sch. of Med., Univ. of Jordan, Amman, Jordan (Al-Taee, A.M.)
Abstract	Chronic Renal Failure (CRF) is a gradual loss of kidney's function over a period of time, ranging from months to years. Unlike other chronic diseases, CRF is not yet thoroughly explored in literature. In this paper, we propose a new clinical decision support system for diagnosing patients with CRF. Several data classification algorithms including Artificial Neural Networks (ANNs), Naïve Bayes and Decision Tree are developed and implemented to diagnose patients with CRF and determine the progression stage of the disease. A clinical dataset of 102 instances is collected from patients' records and used in this study. Performance of the developed CRF diagnosis system is assessed in terms of diagnosis accuracy, sensitivity, and specificity and is evaluated by specialist physicians. Furthermore, the open source Weka software is also used in this study for performance comparison and evaluation purposes. The obtained results showed that the developed decision tree algorithm is the most accurate CRF classifier (92.2%) when compared to all other algorithms/implementations involved in this study.
Sponsorship	IEEE Jordan Sect.
Starting Page	1
Ending Page	6
File Size	1018087
Page Count	6
File Format	PDF
DOI	10.1109/AEECT.2013.6716440
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-03
Publisher Place	Jordan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Data classification Naïve Bayes Chronic renal failure Artificial neural networks Classification algorithms Decision tree Kidney Diseases Accuracy Prediction algorithms Clinical decision support Medical diagnostic imaging
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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