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 modified training set consisting of differences between test results taken at different times was also 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. Support Vector Machines were also tested on this dataset. 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. The effects of oversampling of minority class samples in the training set by generating synthetic examples were tested with ensemble techniques like bagging and random forests. It was observed, that oversampling of diabetic examples, lead to an increased accuracy in diabetic prediction demonstrated by a significantly better F-measure value. ROC curves and the statistical F-measure were used to compare the performance of the different machine learning algorithms.
| Identifer | oai:union.ndltd.org:USF/oai:scholarcommons.usf.edu:etd-3660 |
| Date | 01 June 2006 |
| Creators | Pobi, Shibendra |
| Publisher | Scholar Commons |
| Source Sets | University of South Flordia |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Theses and Dissertations |
| Rights | default |
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