An improved cost-sensitive approach toward the selection of wart treatment methods

AbstractWarts are benign tumors, caused due to the infection of human papillomavirus (HPV). The identification of wart-specific treatment methods is pertaining to major challenges such as class imbalance, prediction accuracy, and biased nature of learning algorithm. In this article, a bagged ensemble of cost-sensitive extra tree classifier (BECSETC) is developed toward the selection of wart-specific treatment methods. BECSETC outperforms the state-of-the-art techniques (SOTA) by a margin of (0 –45, 0\(-\)31.60), (0 –12, 0\(-\)2.6) in terms of sensitivity and specificity which overcome the imbalanced distribution on both immunotherapy and cryotherapy datasets. However, on merged dataset, BECSETC algorithm gave an improvement of 6.04\(-\)10.57%, and 4.63% in terms of sensitivity and specificity, as compared to SOTA techniques.
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - Category: Bioinformatics Source Type: research