Differentiation between atypical anorexia nervosa and anorexia nervosa using machine learning.

ABSTRACT:

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

SUBMITTER: Sandoval-Araujo LE 

PROVIDER: S-EPMC11091846 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature