Applying artificial neural-network model to predict psychiatric symptoms.

Introduction: Mental disorders result in mental disabilities and discomfort in the affected person as they affect both thinking and behavior. Therefore, more vulnerable people must first be identified to improve the psychological level of society. Aim: This study aims to determine the importance of gender, employment, education, place of residence, and age in predicting mental disorders using artificial neural network modeling. Methods: The pattern held between variables in this study will be identified using multilayer feed-forward back-propagation neural networks with five inputs and 10 outputs. To determine the neural network with the least sum of square errors, we evaluated the performance of all neural networks with varying algorithms and different numbers of neurons in the hidden layer. Data were analyzed for 380 people aged 10-82 years using the SPSS software. Results: The optimal neural network model was effective in predicting mental disorders. In this model, the variables of the place of residence, education, age, gender, and employment were important in fitting the optimal model with 34.08, 20.11, 18.93, 14.55, and 12.33%, respectively. The accuracy rate for the neural network model was 99.2%. Conclusion: To achieve further results in improving mental health problems, it is better to focus more on employed, rural, and younger people with a non-tertiary education level.