Abstract
Purpose
In people with obesity, food addiction (FA) tends to be associated with poorer outcomes. Its diagnosis can be challenging in primary care. Based on the SCOFF example, we aim to determine whether a quicker and simpler screening tool for FA in people with obesity could be developed, using artificial intelligence (machine learning).
Methods
The first step was to look for the most discriminating items, among 152 different ones, to differentiate between FA-positive and FA-negative populations of patients with obesity. Items were ranked using the Fast Correlation-Based Filter (FCBF). Retained items were used to test the performance of nine different predictive algorithms. Then, the construction of a graphic tool was proposed.
Results
Data were available for 176 patients. Only three items had a FCBF score > 0.1: “I eat to forget my problems”; “I eat more when I’m alone”; and “I eat sweets or comfort foods”. Naive Bayes classification obtained best predictive performance. Then, we created a 3-item nomogram to predict a positive scoring on the YFAS.
Conclusion
A simple and fast screening tool for detecting high-disordered eating risk is proposed. The next step will be a validation study of the FAST nomogram to ensure its relevance for emotional eating diagnosis.
Level of evidence
Level V, cross-sectional descriptive study.
Clinical trial registry number
NCT02857179 at clinicaltrials.gov.
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Contributions
SI and ED contributed to the study conception and design. Material preparation, data collection and analysis were performed by ST, SI and ED. The first draft of the manuscript was written by SI, and all the authors commented on previous versions of the manuscript. All the authors read and approved the final manuscript.
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Conflict of interest
Dr. Iceta reports grants from Hospices Civils de Lyon, Young Researcher Award; Miss. Guyot reports grants from Institut Benjamin Delessert, SFN ANRT, APICIL, outside the submitted work; Dr. ROBERT reports Medtronic: fees as a consultant, Gore: fees as an expert speaker. The other authors declare that they have no conflict of interest to disclose.
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The study has been approved by the national research ethics committee and has been performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments.
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Informed consent was obtained from participants included in the study.
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Iceta, S., Tardieu, S., Nazare, JA. et al. An artificial intelligence-derived tool proposal to ease disordered eating screening in people with obesity. Eat Weight Disord 26, 2381–2385 (2021). https://doi.org/10.1007/s40519-020-01076-2
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DOI: https://doi.org/10.1007/s40519-020-01076-2