NDLI: Data mining using Artificial Neural network tree

Content Provider	IEEE Xplore Digital Library
Author	Anbananthen, S. Kalaiarasi Sainarayanan, G. Chekima, Ali Teo, Jason
Copyright Year	2005
Description	Author affiliation: School of Engineering and Information Technology, University Malaysia Sabah, 88999 Kota Kinabalu, Malaysia (Anbananthen, S. Kalaiarasi; Sainarayanan, G.; Chekima, Ali; Teo, Jason)
Abstract	Proper diagnosis, classification and prediction of diabetes are essential due to the increasing prevalence of the disease and the increasing cost to control it. Appropriate discovery of knowledge from historical data for this disease would be a valuable tool for clinical researchers. The main purpose of data mining is to gain insight of the data, and extract knowledge (inter-relational patterns) from the data. Applying data mining techniques in diabetic data can facilitate systematic analysis. Artificial Neural network (ANN) has already been applied in a variety of domains with remarkable success. However, it has not has been well utilized in data mining because of the “black box” nature. In this paper we present a method of using ANN in data mining and overcoming the “black box” nature using Decision Tree (DT).
Starting Page	160
Ending Page	164
File Size	868684
Page Count	5
File Format	PDF
ISBN	9781424400119
DOI	10.1109/CCSP.2005.4977180
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-11-14
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Costs Artificial neural networks Neural network Data mining Character recognition Decision tree Diseases Rule extraction Neural networks Diabetes Decision trees Pancreas Medical diagnostic imaging
Content Type	Text
Resource Type	Article

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