Abstract
Warts are benign tumors, caused by human papillomavirus (HPV). The present study mainly emphasis on the selection of suitable methods for the removal of a common and plantar wart. There are numerous wart treatment methods for this disease, among them cryotherapy and immunotherapy are well-known approaches. Identifying the suitable wart treatment method manually is quite challenging. Moreover, existing machine learning (ML) techniques show a poor prediction accuracy towards the selection of wart treatment method, however, the prediction accuracy is not satisfactory and can be further improved. To achieve the same, the current study utilizes the advantage of fuzzy rough set based feature selection (FRFS) to generate the most optimal informative feature space, which in turn makes the ML algorithms more accurate and leads to a better prognosis. The proposed FRFS based Naïve Bayes and FRFS based CART models outperform from the existing model in terms of prediction accuracy.
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Acknowledgements
We would like to thank the Ministry of Human Resource and Development (Grant number 405117002) for providing financial support. We express our sincere thanks to the machine learning and data analytics lab, National Institute of Technology, Tiruchirappalli, for providing the infrastructure facility.
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Mishra, A., Reddy, U.S. Machine learning approach for wart treatment selection: prominence on performance assessment. Netw Model Anal Health Inform Bioinforma 9, 37 (2020). https://doi.org/10.1007/s13721-020-00246-7
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DOI: https://doi.org/10.1007/s13721-020-00246-7