Abstract
Warts 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.
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Data availability
Both immunotherapy and cryotherapy datasets are available in the UCI machine learning repository.
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Acknowledgement
Abinash Mishra would like to thank the Ministry of Human Resource Development (MHRD) for providing financial support (Grant number 405117002). Also, we would like to thank the Machine Learning and Data Analytics Lab, Department of Computer Applications, National Institute of Technology, Tiruchirappalli; National Forensic Sciences University, Gandhinagar and Ministry of Home Affairs for the infrastructure support.
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Mishra, A., Reddy, U.S. & Reddy, A.V. An improved cost-sensitive approach toward the selection of wart treatment methods. Netw Model Anal Health Inform Bioinforma 12, 39 (2023). https://doi.org/10.1007/s13721-023-00433-2
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DOI: https://doi.org/10.1007/s13721-023-00433-2