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A proof-of-concept study applying machine learning methods to putative risk factors for eating disorders: results from the multi-centre European project on healthy eating

Published online by Cambridge University Press:  29 November 2021

I. Krug Open the ORCID record for I. Krug [Opens in a new window] ,
J. Linardon ,
C. Greenwood ,
G. Youssef ,
J. Treasure ,
F. Fernandez-Aranda ,
A. Karwautz ,
G. Wagner ,
D. Collier  and
M. Anderluh
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I. Krug*
Affiliation:
Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, VIC, Australia
J. Linardon
Affiliation:
School of Psychology, Deakin University, Geelong, Australia
C. Greenwood
Affiliation:
Centre for Social and Early Emotional Development, Deakin University, Burwood, Australia Centre for Adolescent Health, Murdoch Children's Research Institute, Royal Children's Hospital, Melbourne, VIC, Australia
G. Youssef
Affiliation:
School of Psychology, Deakin University, Geelong, Australia Centre for Adolescent Health, Murdoch Children's Research Institute, Royal Children's Hospital, Melbourne, VIC, Australia
J. Treasure
Affiliation:
Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
F. Fernandez-Aranda
Affiliation:
Eating Disorders Unit, Department of Psychiatry, University Hospital of Bellvitge, Barcelona, Spain Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain Department of Clinical Sciences, School of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain Psychiatry and Mental Health Group, Neuroscience Program, Institut d'Investigació Biomèdica de Bellvitge—IDIBELL, L'Hospitalet de Llobregat, Spain
A. Karwautz
Affiliation:
Department of Child and Adolescent Psychiatry, Medical University of Vienna, Vienna, Austria
G. Wagner
Affiliation:
Department of Child and Adolescent Psychiatry, Medical University of Vienna, Vienna, Austria
D. Collier
Affiliation:
SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London, UK Discovery Neuroscience Research, Eli Lilly and Company Ltd, Lilly Research Laboratories, Erl Wood Manor, Surrey, UK
M. Anderluh
Affiliation:
Department of Child Psychiatry, University Children's Hospital, University Medical Center Ljubljana, Ljubljana, Slovenia
K. Tchanturia
Affiliation:
Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
V. Ricca
Affiliation:
Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
S. Sorbi
Affiliation:
Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
B. Nacmias
Affiliation:
Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
L. Bellodi
Affiliation:
Department of Neuropsychiatric Sciences, Fondazione Centro San Raffaele del Monte Tabor, Milan, Italy
M. Fuller-Tyszkiewicz
Affiliation:
School of Psychology, Deakin University, Geelong, Australia Centre for Social and Early Emotional Development, Deakin University, Burwood, Australia
*
Author for correspondence: I. Krug, E-mail: isabel.krug@unimelb.edu.au
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Abstract

Background

Despite a wide range of proposed risk factors and theoretical models, prediction of eating disorder (ED) onset remains poor. This study undertook the first comparison of two machine learning (ML) approaches [penalised logistic regression (LASSO), and prediction rule ensembles (PREs)] to conventional logistic regression (LR) models to enhance prediction of ED onset and differential ED diagnoses from a range of putative risk factors.

Method

Data were part of a European Project and comprised 1402 participants, 642 ED patients [52% with anorexia nervosa (AN) and 40% with bulimia nervosa (BN)] and 760 controls. The Cross-Cultural Risk Factor Questionnaire, which assesses retrospectively a range of sociocultural and psychological ED risk factors occurring before the age of 12 years (46 predictors in total), was used.

Results

All three statistical approaches had satisfactory model accuracy, with an average area under the curve (AUC) of 86% for predicting ED onset and 70% for predicting AN v. BN. Predictive performance was greatest for the two regression methods (LR and LASSO), although the PRE technique relied on fewer predictors with comparable accuracy. The individual risk factors differed depending on the outcome classification (EDs v. non-EDs and AN v. BN).

Conclusions

Even though the conventional LR performed comparably to the ML approaches in terms of predictive accuracy, the ML methods produced more parsimonious predictive models. ML approaches offer a viable way to modify screening practices for ED risk that balance accuracy against participant burden.

Keywords

Anorexia nervosabulimia nervosaeating disordersmachine learningrisk and protective factors
Type
Original Article
Information
Psychological Medicine , Volume 53 , Issue 7 , May 2023 , pp. 2913 - 2922
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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