Construction of a machine learning-based prediction model for emotional eating during COVID-19 pandemic among doctors in North China

Objective To construct a prediction model for emotional eating behavior during coronavirus disease 2019 (COVID-19) pandemic among doctors in northern region of China for providing evidence to the promotion of healthy dietary patterns in the doctors. MethodsAn on-site self-administered questionnaire survey was conducted among 2 316 doctors randomly recruited at 39 COVID-19 designated hospitals in 8 provincial-level administrative divions in northern China during May – August 2022. Relevant information of the doctors were collected with a general questionnaire, work-family conflict scale, NEO Five-Factor Inventory and emotional eating scale. Deep neural network (DNN) was used to develop a prediction model for associates of emotional eating during the COVID-19 pandemic in the doctors. ResultsFor 2 094 participants with complete information, the mean overall score of emotional eating during the COVID-19 pandemic was 51.48 ± 17.37 and the dimensional scores were 11.31 ± 4.07 for anger influenced eating, 16.72 ± 7.