The importance of eating behavior risk factors in the primary prevention of obesity has been established. Researchers mostly use\nthe linear model to determine associations among these risk factors. However, in reality, the presence of nonlinearity among these\nfactors causes a bias in the prediction models. The aim of this study was to explore the potential of a hybrid model to predict the\neating behaviors. The hybrid model of structural equation modelling (SEM) and artificial neural networks (ANN) was applied to\nevaluate the prediction model. The SEM analysis was used to check the relationship of the emotional eating scale (EES), body\nshape concern (BSC), and body appreciation scale (BAS) and their effect on different categories of eating behavior patterns (EBP).\nIn the second step, the input and output required for ANN analysis were obtained from SEM analysis and were applied in the\nneural network model. 340 university students participated in this study. The hybrid model (SEM-ANN) was conducted using\nmultilayer perceptron (MLP) with feed-forward network topology. Moreover, Levenbergâ??Marquardt, which is a supervised\nlearning model, was applied as a learning method for MLP training. The tangent/sigmoid function was used for the input layer,\nwhile the linear function was applied for the output layer. The coefficient of determination (R2) and mean square error (MSE) were\ncalculated. Using the hybrid model, the optimal network happened at MLP 3-17-8. It was proved that the hybrid model was\nsuperior to SEM methods because the R2 of the model was increased by 27%, while the MSE was decreased by 9.6%. Moreover, it\nwas found that BSC, BAS, and EES significantly affected healthy and unhealthy eating behavior patterns. Thus, a hybrid approach\ncould be suggested as a significant methodological contribution from a machine learning standpoint, and it can be implemented as\nsoftware to predict models with the highest accuracy.
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