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Prediction of Short Term Adverse Events Occurrence in NB-UVB Phototherapy Treatments using Data mining


Sharifa Mohamed, Bingquan Huang, Tahar Kechadi

Publication Type: 
Refereed Conference Meeting Proceeding
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 selected features and the performance of the classifiers can be significantly improved by sampling 40% of negative class samples in training data to teach the learning algorithms.
Conference Name: 
2017 2nd International Conference on Knowledge Engineering and Applications (ICKEA)
Digital Object Identifer (DOI): 
Publication Date: 
National University of Ireland, Dublin (UCD)
Open access repository: