NDLI: Predicting Juvenile Diabetes from Clinical Test Results

Content Provider	IEEE Xplore Digital Library
Author	Pobi, S. Hall, L.O.
Copyright Year	2006
Description	Author affiliation: Department of Computer Science and Engineering, University of South Florida, Tampa, Fl 33620, e-mail: spobi@cse.usf.edu (Pobi, S.)
Abstract	Two approaches to building models for prediction of the onset of Type 1 diabetes mellitus in juvenile subjects were examined. A set of tests performed immediately before diagnosis was used to build classifiers to predict whether the subject would be diagnosed with juvenile diabetes. A second training set consisting of differences between test results taken at different times was used to build classifiers to predict whether a subject would be diagnosed with juvenile diabetes. Neural networks were compared with decision trees and ensembles of both types of classifiers. The highest known predictive accuracy was obtained when the data was encoded to explicitly indicate missing attributes in both cases. In the latter case, high accuracy was achieved without test results which, by themselves, could indicate diabetes.
Starting Page	2159
Ending Page	2165
File Size	179163
Page Count	7
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.246988
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Diabetes Testing Neural networks Decision trees Classification tree analysis Predictive models Accuracy Machine learning Blood Sugar
Content Type	Text
Resource Type	Article

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