Use of Data Mining Techniques to Predict Short Term Adverse Events Occurrence in NB-UVB Phototherapy Treatments

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Title: Use of Data Mining Techniques to Predict Short Term Adverse Events Occurrence in NB-UVB Phototherapy Treatments
Authors: Mohamed, Sharifa
Huang, Bingquan
Kechadi, Tahar
Permanent link: http://hdl.handle.net/10197/9111
Date: 2017
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.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: International Journal of Machine Learning and Computing
Keywords: Machine Learning & Statistics;Adverse events;Classification;Datamining;Dermatology;Phototherapy;Prediction
Language: en
Status of Item: Peer reviewed
Appears in Collections:Insight Research Collection

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