{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T14:57:58Z","timestamp":1771513078728,"version":"3.50.1"},"reference-count":13,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T00:00:00Z","timestamp":1665705600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007446","name":"King Khalid University","doi-asserted-by":"publisher","award":["GRP.1\/241\/43"],"award-info":[{"award-number":["GRP.1\/241\/43"]}],"id":[{"id":"10.13039\/501100007446","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2022,10,14]]},"abstract":"<jats:p>The use of computer-aided diagnostic (CAD) models has been proposed to aid in the detection and classification of breast cancer. In this work, we evaluated the performance of multilayer perceptron neural network and nonlinear support vector machine models to classify breast cancer nodules. From the contour of 569 samples, ten morphological features were used as input to the classifiers. The average results obtained in the set of 50 simulations performed show that the proposed models showed good performance (all exceeded 90.0%) in terms of accuracy in the test set. The nonlinear support vector machine algorithm stands out when compared to the proposed multilayer perceptron neural network algorithm, with 99% accuracy and a 2% false-negative rate. The neural network model presented lower performance than the nonlinear support vector machine classifier. With the application of the proposed models, the average results obtained are promising in the classification of breast cancer.<\/jats:p>","DOI":"10.1155\/2022\/2143510","type":"journal-article","created":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T22:05:05Z","timestamp":1665785105000},"page":"1-7","source":"Crossref","is-referenced-by-count":4,"title":["Diagnosis of Breast Cancer Using Computational Intelligence Models and IoT Applications"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5042-0690","authenticated-orcid":true,"given":"Mohammed","family":"Alghamdi","sequence":"first","affiliation":[{"name":"College of Computer Science, King Khalid University, Abha 62529, Saudi Arabia"},{"name":"University of Jeddah, Jeddah, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7066-2945","authenticated-orcid":true,"given":"Mohammed","family":"Maray","sequence":"additional","affiliation":[{"name":"College of Computer Science, King Khalid University, Abha 62529, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7964-1051","authenticated-orcid":true,"given":"Malik Bader","family":"Alazzam","sequence":"additional","affiliation":[{"name":"Information Technology College, Ajloun National University, Ajloun, Jordan"},{"name":"The University of Mashreq, Research Center, Baghdad, Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1186\/s12905-017-0376-0"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.4236\/abcr.2016.52009"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfranklin.2006.09.003"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/1422963"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.22214\/ijraset.2021.38582"},{"issue":"3","key":"6","article-title":"Early detection of breast cancer using SVM classifier technique","volume":"1","author":"Y. 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