Differentiation between atypical anorexia nervosa and anorexia nervosa using machine learning.
Objective: Body mass index (BMI) is the primary criterion differentiating anorexia nervosa (AN) and atypical anorexia nervosa despite prior literature indicating few differences between disorders. Machine learning (ML) classification provides us an efficient means of accurately distinguishing betwee...
| Publicado en: | International Journal of Eating Disorders Vol. 57; no. 4; pp. 937 - 951 |
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| Autores principales: | , , , , , , , , |
| Formato: | Artículo |
| Publicado: |
Wiley-Blackwell
Apr2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=176608701&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 176608701 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 02763478 IJD jtl: International Journal of Eating Disorders issn: 02763478 maglogo: Y pubinfo: dt: Apr2024 vid: 57 iid: 4 pid: 480 pub: Wiley-Blackwell artinfo: ui: 176608701 10.1002/eat.24160 ppf: 937 ppct: 14 formats: tig: atl: Differentiation between atypical anorexia nervosa and anorexia nervosa using machine learning. aug: au: Sandoval‐Araujo, Luis E. Cusack, Claire E. Ralph‐Nearman, Christina Glatt, Sofie Han, Yuchen Bryan, Jeffrey Hooper, Madison A. Karem, Andrew Levinson, Cheri A. affil: Department of Psychological & Brain Sciences, University of Louisville, Louisville Kentucky,, USA Department of Biostatistics & Bioinformatics, University of Louisville, Louisville Kentucky,, USA Department of Psychology, Vanderbilt University, Nashville Tennessee,, USA Department of Computer Science & Engineering, University of Louisville, Louisville Kentucky,, USA su: Random forest algorithms Differential diagnosis Body mass index Research funding Receiver operating characteristic curves Questionnaires Logistic regression analysis Descriptive statistics Anorexia nervosa Computer-aided diagnosis Machine learning Decision trees Comparative studies Algorithms Sensitivity & specificity (Statistics) Comorbidity Evaluation sug: subj: Random forest algorithms Differential diagnosis Body mass index Research funding Receiver operating characteristic curves Questionnaires Logistic regression analysis Descriptive statistics Anorexia nervosa Computer-aided diagnosis Machine learning Decision trees Comparative studies Algorithms Sensitivity & specificity (Statistics) Comorbidity Evaluation keyword: anorexia nervosa atypical anorexia nervosa classification diagnosis eating disorders machine learning OSFED anorexia nervosa atypical anorexia nervosa classification diagnosis eating disorders machine learning OSFED ab: Objective: Body mass index (BMI) is the primary criterion differentiating anorexia nervosa (AN) and atypical anorexia nervosa despite prior literature indicating few differences between disorders. Machine learning (ML) classification provides us an efficient means of accurately distinguishing between two meaningful 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. Methods: Using an aggregate sample from seven studies consisting of individuals with AN and atypical AN who completed baseline questionnaires (N = 448), 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. Results: Model performance for all algorithms trained with BMI as a feature was deemed acceptable (mean accuracy = 74.98%, mean area under the receiving operating characteristics curve [AUC] = 74.75%), whereas model performance diminished without BMI (mean accuracy = 59.37%, mean AUC = 59.98%). Discussion: 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. Public Significance: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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