Machine learning approach for wart treatment selection: prominence on performance assessment

AbstractWarts 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 Na ïve Bayes and FRFS based CART models outperform from the existing model in terms of prediction accuracy.
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - Category: Bioinformatics Source Type: research