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Use of Data Mining Techniques to Predict Short Term Adverse Events Occurrence in NB-UVB Phototherapy Treatments
Date Issued
2018-04
Date Available
2017-12-14T15:19:50Z
Abstract
The prediction of short term adverse events occurrence in phototherapy treatment is important for the dermatologists who administrate phototherapy to adjust the treatment and standardize the clinical outcomes. Recently, a modeling technique which can detect the potential short term adverse events occurrence in phototherapy treatments is required for clinicians. Based on data mining, this study tends to explore the significant features and the class distribution of training data for the short term adverse events occurrence prediction in NB-UVB phototherapy treatments. The experimental results highlight that acceptable prediction accuracy can be achieved by using the significant features and the performance of the classifiers can be significantly improved by sampling 40% of negative class samples in training data, hyper parameter tuning of classifiers and use of stacked classifiers in creating prediction models.
Sponsorship
Science Foundation Ireland
Other Sponsorship
Insight Research Centre
Type of Material
Journal Article
Publisher
International Journal of Machine Learning and Computing
Journal
International Journal of Machine Learning and Computing
Volume
8
Issue
2
Start Page
104
End Page
111
Language
English
Status of Item
Peer reviewed
ISSN
2010-3700
This item is made available under a Creative Commons License
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2
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