{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T15:54:07Z","timestamp":1768406047509,"version":"3.49.0"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T00:00:00Z","timestamp":1695081600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T00:00:00Z","timestamp":1695081600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004541","name":"Ministry of Education, India","doi-asserted-by":"publisher","award":["405117002"],"award-info":[{"award-number":["405117002"]}],"id":[{"id":"10.13039\/501100004541","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-16621-1","type":"journal-article","created":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T07:02:37Z","timestamp":1695106957000},"page":"33393-33419","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A customized cost penalized boosting approach for the selection of wart treatment methods"],"prefix":"10.1007","volume":"83","author":[{"given":"Abinash","family":"Mishra","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6478-3839","authenticated-orcid":false,"given":"Srinivasulu Reddy","family":"U","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Venkataswamy Reddy","family":"A","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,19]]},"reference":[{"key":"16621_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13721-020-00246-7","volume":"9","author":"A Mishra","year":"2020","unstructured":"Mishra A, Reddy US (2020) Machine learning approach for wart treatment selection: prominence on performance assessment. Netw Model Anal Health Inform Bioinforma 9:1\u201314","journal-title":"Netw Model Anal Health Inform Bioinforma"},{"issue":"1","key":"16621_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-22612-2","volume":"8","author":"S Keel","year":"2018","unstructured":"Keel S, Lee PY, Scheetz J, Li Z, Kotowicz MA, MacIsaac RJ, He M (2018) Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study. Sci Rep 8(1):1\u20136","journal-title":"Sci Rep"},{"key":"16621_CR3","doi-asserted-by":"crossref","unstructured":"Sizonenko PC (1978) Endocrinology in preadolescents and adolescents: I. hormonal changes during normal puberty. Am J Dis Child 132(7):704\u2013712","DOI":"10.1001\/archpedi.1978.02120320064015"},{"issue":"23","key":"16621_CR4","doi-asserted-by":"publisher","first-page":"2668","DOI":"10.1016\/j.jacc.2018.03.521","volume":"71","author":"KW Johnson","year":"2018","unstructured":"Johnson KW, Soto JT, Glicksberg BS, Shameer K, Miotto R, Ali M, Ashley E, Dudley JT (2018) Artificial intelligence in cardiology. J Am Coll Cardiol 71(23):2668\u20132679","journal-title":"J Am Coll Cardiol"},{"key":"16621_CR5","doi-asserted-by":"crossref","unstructured":"Vivaldi N, Caiola M, Solarana K, Ye M (2021) Evaluating performance of eeg data-driven machine learning for traumatic brain injury classification. IEEE Transactions on Biomedical Engineering","DOI":"10.1109\/TBME.2021.3062502"},{"key":"16621_CR6","doi-asserted-by":"crossref","unstructured":"Thurnhofer-Hemsi K, Dom\u00ednguez E (2020) A convolutional neural network framework for accurate skin cancer detection. Neural Process Lett, 1\u201321","DOI":"10.1007\/s11063-020-10364-y"},{"issue":"2","key":"16621_CR7","doi-asserted-by":"publisher","first-page":"1907","DOI":"10.1007\/s11063-018-09976-2","volume":"50","author":"SK Pandey","year":"2019","unstructured":"Pandey SK, Janghel RR (2019) Recent deep learning techniques, challenges and its applications for medical healthcare system: A review. Neural Process Lett 50(2):1907\u20131935","journal-title":"Neural Process Lett"},{"key":"16621_CR8","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.compbiomed.2017.01.001","volume":"81","author":"F Khozeimeh","year":"2017","unstructured":"Khozeimeh F, Alizadehsani R, Roshanzamir M, Khosravi A, Layegh P, Nahavandi S (2017) An expert system for selecting wart treatment method. Comput Biol Med 81:167\u2013175","journal-title":"Comput Biol Med"},{"issue":"4","key":"16621_CR9","doi-asserted-by":"publisher","first-page":"819","DOI":"10.1016\/j.bbe.2018.06.007","volume":"38","author":"SB Akben","year":"2018","unstructured":"Akben SB (2018) Predicting the success of wart treatment methods using decision tree based fuzzy informative images. Biocybernetics Biomed Eng 38(4):819\u2013827","journal-title":"Biocybernetics Biomed Eng"},{"issue":"1","key":"16621_CR10","doi-asserted-by":"publisher","first-page":"73","DOI":"10.3934\/mfc.2019006","volume":"2","author":"W Jia","year":"2019","unstructured":"Jia W, Deng Y, Xin C, Liu X, Pedrycz W (2019) A classification algorithm with linear discriminant analysis and axiomatic fuzzy sets. Math Found Comput 2(1):73\u201381","journal-title":"Math Found Comput"},{"issue":"7","key":"16621_CR11","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1007\/s10916-019-1343-0","volume":"43","author":"M Abdar","year":"2019","unstructured":"Abdar M, Wijayaningrum VN, Hussain S, Alizadehsani R, Plawiak P, Acharya UR, Makarenkov V (2019) Iapso-airs: A novel improved machine learning-based system for wart disease treatment. J Med Syst 43(7):220","journal-title":"J Med Syst"},{"key":"16621_CR12","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.compbiomed.2019.04.001","volume":"108","author":"MM Ghiasi","year":"2019","unstructured":"Ghiasi MM, Zendehboudi S (2019) Decision tree-based methodology to select a proper approach for wart treatment. Comput Biol Med 108:400\u2013409","journal-title":"Comput Biol Med"},{"key":"16621_CR13","doi-asserted-by":"crossref","unstructured":"Rahman M, Zhou Y, Wang S, Rogers J et al (2020) Wart treatment decision support using support vector machine. I.J. Intell Syst Appl 12(1):1\u201311","DOI":"10.5815\/ijisa.2020.01.01"},{"key":"16621_CR14","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp 785\u2013794","DOI":"10.1145\/2939672.2939785"},{"key":"16621_CR15","unstructured":"Common wart Child. https:\/\/www.skinsight.com\/skin-conditions\/child\/common-wart-verruca-vulgaris. Accessed 01 Nov 2021"},{"key":"16621_CR16","unstructured":"Plantar wart Child. https:\/\/www.skinsight.com\/skin-conditions\/child\/plantar-wart-hpv. Accessed 01 Nov 2021"},{"key":"16621_CR17","doi-asserted-by":"crossref","unstructured":"Mazlin TT, Sallehuddin R , Zuriahati M (2019) Utilization of filter feature selection with support vector machine for tumours classification. In: IOP Conference Series: Materials Science and Engineering, vol 551, p 012062. IOP Publishing","DOI":"10.1088\/1757-899X\/551\/1\/012062"},{"key":"16621_CR18","unstructured":"Lazy Predict Documentation. https:\/\/lazypredict.readthedocs.io\/_\/downloads\/en\/latest\/pdf\/. Accessed 28 Sept 2022"},{"issue":"1","key":"16621_CR19","doi-asserted-by":"publisher","first-page":"20804","DOI":"10.1038\/s41598-022-25089-2","volume":"12","author":"SP Praveen","year":"2022","unstructured":"Praveen SP, Srinivasu PN, Shafi J, Wozniak M, Ijaz MF (2022) Resnet-32 and fastai for diagnoses of ductal carcinoma from 2d tissue slides. Sci Rep 12(1):20804","journal-title":"Sci Rep"},{"key":"16621_CR20","doi-asserted-by":"crossref","unstructured":"Nugroho HW, Adji TB, Setiawan NA (2018) Random forest weighting based feature selection for c4. 5 algorithm on wart treatment selection method. Int. J. Adv. Sci. Eng. Inf. Technol 8(5):1858","DOI":"10.18517\/ijaseit.8.5.6504"},{"key":"16621_CR21","unstructured":"Khozeimeh F, Alizadehsani R, Roshanzamir M, Khosravi A, Layegh P, Nahavandi S (2023) An Expert System for Selecting Wart Treatment Method. https:\/\/archive.ics.uci.edu\/ml\/datasets\/Immunotherapy+Dataset. Accessed 04 Jan 2018"},{"key":"16621_CR22","unstructured":"Khozeimeh F, Alizadehsani R, Roshanzamir M, Khosravi A, Layegh P, Nahavandi S (2023) An Expert System for Selecting Wart Treatment Method. https:\/\/archive.ics.uci.edu\/ml\/datasets\/Cryotherapy+Dataset+. Accessed 04 Jan 2018"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16621-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-16621-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16621-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T06:45:55Z","timestamp":1709880355000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-16621-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,19]]},"references-count":22,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["16621"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-16621-1","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,19]]},"assertion":[{"value":"1 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 August 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 August 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"Authors declare that they have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}]}}