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A customized cost penalized boosting approach for the selection of wart treatment methods

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Abstract

Warts are benign tumors infected by the Human Papillomavirus. Physicians and medical practitioners are endeavoring to identify the best wart treatment method. The present study finds the response of well-known wart treatment methods, namely immunotherapy and cryotherapy, towards the removal of predominant wart types such as plantar and common warts. The present study utilized the optimal feature space generated by the measure of Fisher score, information gain, and univariate statistical test. In addition, the proposed method finds the customized cost in terms of class weighted and non-class weighted to reduce the miss-classified instances for the positive class sample. The class-weighted and non-class-weighted approaches explicitly incorporated with the well-known classification algorithm extreme gradient boosting approach, which provides a maximum measure of true positive rate, true negative rate, positive predicted value, F-measure, and area under receiver operating characteristic curve of 100.00, 100.00, 100.00, 80.00, and 82.00% respectively on immunotherapy dataset, 100.00, 100.00, 100.00, 100.00, 92.00% respectively on cryotherapy dataset. While validating the performance on the benchmark dataset with the state-of-the-art approach, the proposed model gives an improvement of 6.40% to a maximum of 43.00% in terms of specificity on the immunotherapy dataset. However, the proposed model improves 3.33 - 30%, 5.70 -30%, and 0 - 31% in terms of accuracy, sensitivity, and specificity, respectively on cryotherapy dataset. Also, the proposed framework achieved a maximum sensitivity of 91.30%, which dominates the existing state-of-the-art approaches by a margin of 1.87% and 10.82%, respectively on the merged dataset.

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Data Availability

Immunotherapy and Cryotherapy datasets are well-known wart treatment method for the removal of plantar and common warts. This study analysed the efficiency of machine learning models on benchmark datasets available on UCI Machine Learning Repository [21, 22].

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Acknowledgements

Abinash Mishra would like to thank the Ministry of Human Resource Development (MHRD) for providing the 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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Authors and Affiliations

  1. School of Cyber Security & Digital Forensics, National Forensic Sciences University, Delhi Campus, Cyber Security Lab, 110085, Delhi, India

    Abinash Mishra

  2. Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Machine Learning and Data Analytics Lab, 620015, Tamil Nadu, India

    Srinivasulu Reddy U

  3. Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Big Data Analytics Lab, 620015, Tamil Nadu, India

    Venkataswamy Reddy A

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  1. Abinash Mishra
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  3. Venkataswamy Reddy A
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Correspondence to Srinivasulu Reddy U.

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Mishra, A., U, S.R. & A, V.R. A customized cost penalized boosting approach for the selection of wart treatment methods. Multimed Tools Appl 83, 33393–33419 (2024). https://doi.org/10.1007/s11042-023-16621-1

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