{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T14:49:37Z","timestamp":1709390977365},"reference-count":33,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2021,11,4]],"date-time":"2021-11-04T00:00:00Z","timestamp":1635984000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2022,2,28]]},"abstract":"Summary<\/jats:title>In current years, the death rate from skin cancers (SCs) tends to develop pretty. Various research verified that SC rank third as a deadliest disease, after breast and lung cancer. It will become vital to diagnose this malignancy at an early stage. The objective of this research is to mix machine learning and soft computing techniques to gain higher accuracy within the prediction of SC. To play out the exploration work, we utilized two data sets, one from \u201cSave Life Hospital,\u201d India, and the other is the UCI repository skin cancer data set. In this article, three meta\u2010heuristic algorithms, the FS_GA, the FS_PSO, and the FS_ACO, were used to select the best features from the data set provided to it. The AFRG_algorithm generates a set of fuzzy rules automatically and the RR_algorithm reduces certain fuzzy rules from the fuzzy system. For the SCC_dataset, the end accuracy obtained was 97.67%, 98.45%, and 99.22%. For the UCI_dataset, the end accuracy obtained was 98.81%, 99.72%, and 99.67%. Experimental results on the used datasets show that the proposed method strikingly improves the forecast exactitude of skin malignancy.<\/jats:p>","DOI":"10.1002\/cpe.6694","type":"journal-article","created":{"date-parts":[[2021,11,4]],"date-time":"2021-11-04T07:03:50Z","timestamp":1636009430000},"update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An evolutionary algorithm based feature selection and fuzzy rule reduction technique for the prediction of skin cancer"],"prefix":"10.1002","volume":"34","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-5936-4739","authenticated-orcid":false,"given":"Saurabh","family":"Jha","sequence":"first","affiliation":[{"name":"Department of Computer Applications National Institute of Technology Jamshedpur Jamshedpur India"}]},{"given":"Ashok Kumar","family":"Mehta","sequence":"additional","affiliation":[{"name":"Department of Computer Applications National Institute of Technology Jamshedpur Jamshedpur India"}]}],"member":"311","published-online":{"date-parts":[[2021,11,4]]},"reference":[{"key":"e_1_2_9_2_1","first-page":"e1265","article-title":"Global incidence and mortality of skin cancer by histological subtype and its relationship with the human development index (HDI); an ecology study in 2018","volume":"6","author":"Khazaei Z","year":"2019","journal-title":"World Cancer Res J"},{"key":"e_1_2_9_3_1","first-page":"325","article-title":"A fuzzy logic based approach for prediction of squamous cell carcinoma","volume":"1154","author":"Jha S","year":"2020","journal-title":"Soft Comput Theor Appl"},{"key":"e_1_2_9_4_1","unstructured":"Skin cancer (Non\u2010Melanoma): introduction; 2019. https:\/\/https:\/\/www.cancer.net\/cancer\u2010types\/skin\u2010cancer\u2010non\u2010melanoma\/introduction."},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.oraloncology.2019.104473"},{"issue":"20","key":"e_1_2_9_6_1","first-page":"e4","article-title":"A fuzzy logic based approach for prediction of basal cell carcinoma and squamous cell carcinoma among the data of skin cancer","volume":"5","author":"Jha S","year":"2019","journal-title":"EAI Endorsed Trans Pervasive Health Technol"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2019.06.005"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2019.100282"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phrs.2019.104584"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jphotobiol.2020.111847"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phrs.2019.104499"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105475"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.113129"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2019.01.006"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.canep.2019.01.004"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmedr.2018.09.017"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pacs.2017.05.003"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.08.010"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2014.06.024"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.04.209"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2014.11.029"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2008.09.007"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.amepre.2005.04.007"},{"key":"e_1_2_9_24_1","unstructured":"What is fuzzy logic? 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