{"id":1519,"date":"2022-12-20T12:36:14","date_gmt":"2022-12-20T12:36:14","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=1519"},"modified":"2022-12-21T13:36:26","modified_gmt":"2022-12-21T13:36:26","slug":"vol20-no2-art2","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol20-no2\/vol20-no2-art2\/","title":{"rendered":"Benign and Malign Breast Cancer Classification Using Support Vector Machines Optimized with Particle Swarm and Genetic Algorithms"},"content":{"rendered":"<p>Uriel Abe Contardi <a href=\"https:\/\/orcid.org\/0000-0001-7865-2971\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, Paulo Rog\u00e9rio Scalassara <a href=\"https:\/\/orcid.org\/0000-0001-7169-954X\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>&#038; Douglas Vieira Thomaz <a href=\"https:\/\/orcid.org\/0000-0003-0000-3466\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a><\/p>\n<p><strong>Abstract:<\/strong> Breast cancer is a neoplastic disease that can be diagnosed either as benign or malign according to the growth-rate of the neoplastic lesion. Owing to the relevance of obtaining better detection tools, this work describes the development and optimization of support vector machines for the classification of the types of such cancer. Tests were performed using the breast cancer dataset of the University of Wisconsin Hospitals, USA, available at the Machine Learning Repository of the University of California Irvine. The radial basis function kernel was selected for the classifier and its hyperparameters were refined using two methods: particle swarm optimization and genetic algorithms. The results for the first method exhibited 97.71% accuracy, 96.30% sensitivity, and 98.65% of selectivity. On the other hand, using the second method, the accuracy was 95.78%, with sensitivity and selectivity of 96.73% and 95.25%, respectively. Therefore, there is an indication that these search algorithms are viable tools to optimize machine learning models for the purpose of breast cancer classification.<\/p>\n<p><strong>Keywords:<\/strong> evolutionary algorithm, swarm intelligence algorithm, neural models, optimization, neoplastic tumor.<\/p>\n<p><strong>DOI code:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol20-no2-art2\">10.21528\/lnlm-vol20-no2-art2<\/a><\/p>\n<p><strong>PDF file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2022\/12\/vol20-no2-art2.pdf\">vol20-no2-art2.pdf<\/a><\/p>\n<p><strong>BibTex file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2022\/12\/vol20-no2-art2.bib\">vol20-no2-art2.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Uriel Abe Contardi , Paulo Rog\u00e9rio Scalassara &#038; Douglas Vieira Thomaz Abstract: Breast cancer is a neoplastic disease that can be diagnosed either as benign or malign according to the growth-rate of the neoplastic lesion. Owing to the relevance of <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol20-no2\/vol20-no2-art2\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":1512,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1519","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Benign and Malign Breast Cancer Classification Using Support Vector Machines Optimized with Particle Swarm and Genetic Algorithms - Learning and NonLinear Models<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol20-no2\/vol20-no2-art2\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Benign and Malign Breast Cancer Classification Using Support Vector Machines Optimized with Particle Swarm and Genetic Algorithms - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"Uriel Abe Contardi , Paulo Rog\u00e9rio Scalassara &#038; Douglas Vieira Thomaz Abstract: Breast cancer is a neoplastic disease that can be diagnosed either as benign or malign according to the growth-rate of the neoplastic lesion. 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