Origin: Warts are produced and developed on the human body due to infection induced by Human Papillomavirus. The most influenced zone of warts are hands and feet particularly, which is bit irritating and difficult to recoup in later stages. The major challenge in treating warts is the diversity of treatment method applicable on different patients, so it becomes difficult to recognize specific treatment method to be adopted in order to treat this infection. Ramifications of machine learning techniques in the medical domain have become crucial nowadays for early disease detection and developing expert systems. Objective: This research work focuses on enhancing predictive accuracy of J48, which is a binary decision tree based classifier by adding attributes based on genetic programming. These genetically tuned attribute construction not only just upgrades the classification capabilities of J48 classifier but also additionally expand the information space, intending J48 for giving more exact predictions for wart treatment method identification. Method: For their experimental setup, authors have chosen immunotherapy and cryotherapy datasets from UCI machine learning repositories, which includes instances of patients responses against treated with immunotherapy and cryotherapy methods for both plantar and common warts. The investigation has been led with the help of WEKA tool, which is an open source for performing data mining operations. Finding: After experimentation, it is found after inclusion of attributes generated through genetic programming, the classification accuracy of J48 can be increased by a substantial amount with less error rate. The result shows significant performance improvements in classification accuracy of J48 by 82.22% to 96.66% and 93.33% to 98.88% for immunotherapy and cryotherapy datasets, implemented with J48 and J48+GA respectively.
Khatri, S., Arora, D., & Kumar, A. (2018). Enhancing Decision Tree Classification Accuracy through Genetically Programmed Attributes for Wart Treatment Method Identification. In Procedia Computer Science (Vol. 132, pp. 1685–1694). Elsevier B.V. https://doi.org/10.1016/j.procs.2018.05.141