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Machine learning (ML) classification provides us an efficient means of accurately distinguishing between two\n                      <jats:italic>meaningful<\/jats:italic>\n                      classes given any number of features. The aim of the present study was to determine if ML algorithms can accurately distinguish AN and atypical AN given an ensemble of features excluding BMI, and if not, if the inclusion of BMI enables ML to accurately classify between the two.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Using an aggregate sample from seven studies consisting of individuals with AN and atypical AN who completed baseline questionnaires (\n                      <jats:italic>N\u2009=<\/jats:italic>\n                      \u2009448), we used logistic regression, decision tree, and random forest ML classification models each trained on two datasets, one containing demographic, eating disorder, and comorbid features without BMI, and one retaining all features and BMI.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Model performance for all algorithms trained with BMI as a feature was deemed acceptable (mean accuracy\u2009=\u200974.98%, mean area under the receiving operating characteristics curve [AUC]\u2009=\u200974.75%), whereas model performance diminished without BMI (mean accuracy\u2009=\u200959.37%, mean AUC\u2009=\u200959.98%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Model performance was acceptable, but not strong, if BMI was included as a feature; no other features meaningfully improved classification. When BMI was excluded, ML algorithms performed poorly at classifying cases of AN and atypical AN when considering other demographic and clinical characteristics. Results suggest a reconceptualization of atypical AN should be considered.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Public Significance<\/jats:title>\n                    <jats:p>There is a growing debate about the differences between anorexia nervosa and atypical anorexia nervosa as their diagnostic differentiation relies on BMI despite being similar otherwise. We aimed to see if machine learning could distinguish between the two disorders and found accurate classification only if BMI was used as a feature. This finding calls into question the need to differentiate between the two disorders.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/eat.24160","type":"journal-article","created":{"date-parts":[[2024,2,14]],"date-time":"2024-02-14T03:52:41Z","timestamp":1707882761000},"page":"937-950","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Differentiation between atypical anorexia nervosa and anorexia nervosa using machine learning"],"prefix":"10.1002","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9166-6056","authenticated-orcid":false,"given":"Luis E.","family":"Sandoval\u2010Araujo","sequence":"first","affiliation":[{"name":"Department of Psychological &amp; Brain Sciences University of Louisville  Louisville Kentucky USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2652-3737","authenticated-orcid":false,"given":"Claire E.","family":"Cusack","sequence":"additional","affiliation":[{"name":"Department of Psychological &amp; Brain Sciences University of Louisville  Louisville Kentucky USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4989-153X","authenticated-orcid":false,"given":"Christina","family":"Ralph\u2010Nearman","sequence":"additional","affiliation":[{"name":"Department of Psychological &amp; Brain Sciences University of Louisville  Louisville Kentucky USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1757-6463","authenticated-orcid":false,"given":"Sofie","family":"Glatt","sequence":"additional","affiliation":[{"name":"Department of Psychological &amp; Brain Sciences University of Louisville  Louisville Kentucky USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuchen","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Biostatistics &amp; Bioinformatics University of Louisville  Louisville Kentucky USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffrey","family":"Bryan","sequence":"additional","affiliation":[{"name":"Department of Psychological &amp; Brain Sciences University of Louisville  Louisville Kentucky USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Madison A.","family":"Hooper","sequence":"additional","affiliation":[{"name":"Department of Psychology Vanderbilt University  Nashville Tennessee USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Karem","sequence":"additional","affiliation":[{"name":"Department of Computer Science &amp; Engineering University of Louisville  Louisville Kentucky USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2359-5206","authenticated-orcid":false,"given":"Cheri A.","family":"Levinson","sequence":"additional","affiliation":[{"name":"Department of Psychological &amp; Brain Sciences University of Louisville  Louisville Kentucky USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,2,14]]},"reference":[{"key":"e_1_2_10_2_1","unstructured":"American Medical Association. 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