Intelligent Systems Developed for the Early Detection of Chronic Kidney Disease
Joint Authors
Chang, Yen-Chun
Wang, Shin-An
Chen, Renee Y.
Chen, Li-Chien
Chiu, Ruey Kei
Source
Advances in Artificial Neural Systems
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-01-09
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Abstract EN
This paper aims to construct intelligence models by applying the technologies of artificial neural networks including back-propagation network (BPN), generalized feedforward neural networks (GRNN), and modular neural network (MNN) that are developed, respectively, for the early detection of chronic kidney disease (CKD).
The comparison of accuracy, sensitivity, and specificity among three models is subsequently performed.
The model of best performance is chosen.
By leveraging the aid of this system, CKD physicians can have an alternative way to detect chronic kidney diseases in early stage of a patient.
Meanwhile, it may also be used by the public for self-detecting the risk of contracting CKD.
American Psychological Association (APA)
Chiu, Ruey Kei& Chen, Renee Y.& Wang, Shin-An& Chang, Yen-Chun& Chen, Li-Chien. 2013. Intelligent Systems Developed for the Early Detection of Chronic Kidney Disease. Advances in Artificial Neural Systems،Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-479814
Modern Language Association (MLA)
Chiu, Ruey Kei…[et al.]. Intelligent Systems Developed for the Early Detection of Chronic Kidney Disease. Advances in Artificial Neural Systems No. 2013 (2013), pp.1-7.
https://search.emarefa.net/detail/BIM-479814
American Medical Association (AMA)
Chiu, Ruey Kei& Chen, Renee Y.& Wang, Shin-An& Chang, Yen-Chun& Chen, Li-Chien. Intelligent Systems Developed for the Early Detection of Chronic Kidney Disease. Advances in Artificial Neural Systems. 2013. Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-479814
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-479814