Background: This study aimed to investigate the quality of life (QOL) and mental health (MH) post-trauma in the population over 15 years.
Methods: A cross-sectional population-based study, the cluster sampling method was used and 3880 people were interviewed randomly selected individuals between over 15 years in each household in the city of Kashan. Data were analyzed using chi-square, t-test. After data collection, a radial basis function neural network (RBFNN) architecture is exploited to predict the QOL category and MH status after traumatic injuries.
Results :The rate of trauma was 70.64 (62.60-78.70) in 1000 annually, and 77.73% were male. 38.3% of people with trauma have suspected of having mental disorder and 53.3% of people with injury were in good condition of QOL. The risk of suspected MH disorders in people with trauma during the last year was 1.2(0.96-1.61), and the risk of bad QOL was 2.6(1.8-3.7). The obtained results reveal that the RBFNN model can predict the QOL category and MH status with a high level of accuracy (maximum 1% predicting error).
Conclusion :This study reveals several parameters associated with the MH and QOL after trauma. This parameter can be used in prediction outcomes and used to evaluate the care of people with injury. It can also be concluded that the RBFNN predictor can be used for predicting the QOL category and MH status with reasonable authenticity, efficiency, and accuracy.