{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T10:23:32Z","timestamp":1789122212626,"version":"build-2803163510"},"reference-count":40,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T00:00:00Z","timestamp":1628899200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2021,8,14]]},"abstract":"<jats:p>\n                    Alopecia areata is defined as an autoimmune disorder that results in hair loss. The latest worldwide statistics have exhibited that alopecia areata has a prevalence of 1 in 1000 and has an incidence of 2%. Machine learning techniques have demonstrated potential in different areas of dermatology and may play a significant role in classifying alopecia areata for better prediction and diagnosis. We propose a framework pertaining to the classification of healthy hairs and alopecia areata. We used 200 images of healthy hairs from the Figaro1k dataset and 68 hair images of alopecia areata from the Dermnet dataset to undergo image preprocessing including enhancement and segmentation. This was followed by feature extraction including texture, shape, and color. Two classification techniques, i.e., support vector machine (SVM) and\n                    <jats:inline-formula>\n                      <a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\">\n                        <a:mi>k<\/a:mi>\n                      <\/a:math>\n                    <\/jats:inline-formula>\n                    -nearest neighbor (KNN), are then applied to train a machine learning model with 70% of the images. The remaining image set was used for the testing phase. With a 10-fold cross-validation, the reported accuracies of SVM and KNN are 91.4% and 88.9%, respectively. Paired sample\n                    <jats:inline-formula>\n                      <c:math xmlns:c=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\">\n                        <c:mi>T<\/c:mi>\n                      <\/c:math>\n                    <\/jats:inline-formula>\n                    -test showed significant differences between the two accuracies with a\n                    <jats:inline-formula>\n                      <e:math xmlns:e=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\">\n                        <e:mi>p<\/e:mi>\n                        <e:mo>&lt;<\/e:mo>\n                        <e:mn>0.001<\/e:mn>\n                      <\/e:math>\n                    <\/jats:inline-formula>\n                    . SVM generated higher accuracy (91.4%) as compared to KNN (88.9%). The findings of our study demonstrate potential for better prediction in the field of dermatology.\n                  <\/jats:p>","DOI":"10.1155\/2021\/1102083","type":"journal-article","created":{"date-parts":[[2021,8,16]],"date-time":"2021-08-16T13:45:46Z","timestamp":1629121546000},"page":"1-10","source":"Crossref","is-referenced-by-count":50,"title":["Classification Framework for Healthy Hairs and Alopecia Areata: A Machine Learning (ML) Approach"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2501-1481","authenticated-orcid":true,"given":"Choudhary Sobhan","family":"Shakeel","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Ziauddin University, Faculty of Engineering, Science, Technology and Management, Karachi, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7351-2494","authenticated-orcid":true,"given":"Saad Jawaid","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Ziauddin University, Faculty of Engineering, Science, Technology and Management, Karachi, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0437-6924","authenticated-orcid":true,"given":"Beenish","family":"Chaudhry","sequence":"additional","affiliation":[{"name":"School of Computing and Informatics, University of Louisiana at Lafayette, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0958-137X","authenticated-orcid":true,"given":"Syeda Fatima","family":"Aijaz","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Ziauddin University, Faculty of Engineering, Science, Technology and Management, Karachi, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4690-1982","authenticated-orcid":true,"given":"Umer","family":"Hassan","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Ziauddin University, Faculty of Engineering, Science, Technology and Management, Karachi, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.det.2017.08.003"},{"issue":"1","key":"2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/nrdp.2017.11","article-title":"Alopecia areata","volume":"3","author":"C. 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