{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T02:41:30Z","timestamp":1772160090968,"version":"3.50.1"},"reference-count":18,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T00:00:00Z","timestamp":1726272000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Otolaryngology"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Machine learning has been effective in other areas of medicine, this study aims to investigate this with regards to HNC and identify which algorithm works best to classify malignant patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>An observational cohort study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting<\/jats:title>\n                    <jats:p>Queen Elizabeth University Hospital.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Participants<\/jats:title>\n                    <jats:p>Patients who were referred via the USOC pathway between January 2019 and May 2021.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Main Outcome Measures<\/jats:title>\n                    <jats:p>Predicting the diagnosis of patients from three categories, benign, potential malignant and malignant, using demographics and symptoms data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The classic statistical method of ordinal logistic regression worked best on the data, achieving an AUC of 0.6697 and balanced accuracy of 0.641. The demographic features describing recreational drug use history and living situation were the most important variables alongside the red flag symptom of a neck lump.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Further studies should aim to collect larger samples of malignant and pre\u2010malignant patients to improve the class imbalance and increase the performance of the machine learning models.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/coa.14220","type":"journal-article","created":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T06:20:31Z","timestamp":1726294831000},"page":"31-38","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning in Clinical Diagnosis of Head and Neck\u00a0Cancer"],"prefix":"10.1111","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5099-9302","authenticated-orcid":false,"given":"Hollie","family":"Black","sequence":"first","affiliation":[{"name":"Department of Naval Architecture, Ocean and Marine Engineering University of Strathclyde  Glasgow UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3652-0513","authenticated-orcid":false,"given":"David","family":"Young","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics University of Strathclyde  Glasgow UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4036-4119","authenticated-orcid":false,"given":"Alexander","family":"Rogers","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology, Head and Neck Surgery Queen Elizabeth University Hospital  Glasgow UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5000-4913","authenticated-orcid":false,"given":"Jenny","family":"Montgomery","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology, Head and Neck Surgery Queen Elizabeth University Hospital  Glasgow UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,9,14]]},"reference":[{"key":"e_1_2_10_2_1","unstructured":"\u201cYour urgent suspected cancer referral: Cancer Research UK \u201d (2022) https:\/\/www.cancerresearchuk.org\/cancer\u2010symptoms\/what\u2010is\u2010an\u2010urgent\u2010referral."},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00405-020-05897-w"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1308\/rcsann.2018.0168"},{"key":"e_1_2_10_5_1","doi-asserted-by":"crossref","unstructured":"T.Tikka K.Kavanagh A.Lowit et al. \u201cHead and Neck Cancer Risk Calculator (HaNC\u2010RC)\u2014v.2. 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