Document Type


Source Publication Title

Proceedings of the 2019 IISE Annual Conference. May 18-21, 2019, Orlando, FL, USA


Warts are non-cancerous tumors that can appear on the top layer of skin of different parts of the human body. For the treatment of warts, cryotherapy, a method of medical therapy that involves the application of extremely low temperatures to destroy anomalous or diseased tissue, has been commonly adopted in practice. However, the effectiveness of this treatment method varies from patient to patient. By utilizing a secondary data set which was collected from 90 patients in a dermatology clinic, this study aims to develop an accurate classification model to predict the effectiveness of cryotherapy on individual patients. To sort out the important factors, Fuzzy Entropy and Mutual Information based feature selection method has been utilized. Several machine learning algorithms have been deployed and the classification performances of these algorithms have been examined by 10-fold cross-validation method. The Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel and K-Nearest Neighbors (KNN) algorithms have been found to provide promising results with an average prediction accuracy of 95.11% and 96.78%, respectively. There are several potential benefits of this study. The classification model will assist the physicians as a decision support tool to determine when to select cryotherapy over other available wart treatment methods for each unique patient. Furthermore, valuable time and hospitals’ resources can be saved by reducing readmissions and possible side effects may be avoided for some patients due to inappropriate selection of cryotherapy as a treatment process.


Engineering | Operations Research, Systems Engineering and Industrial Engineering

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Available for download on Wednesday, January 01, 3000