Predict of Happiness Based on Resilient Components Using Adaptive Neuro-Fuzzy Inference System in Female-headed households

Female-headed households have many physical and mental problems. Today, methods of mathematical computing can be used as reliable tool for predicting individuals' psychological problems. Targeting and optimism about the future are important components of resiliency that affect women's happiness. Happiness and consequently depression in female-headed households is a disease so it needs to be identified and predicted. The aim of this study was to predict happiness in female-headed based on resiliency components using ANFIS. In this study, the measuring instrument was the Conner and Davidson Resiliency and the Oxford Happiness Questionnaire. The statistical population included 50 female-headed households. The mean happiness and resiliency in female-headed households was 39.8 and 40.26, respectively. After evaluating the models, the final model of happiness prediction based on resiliency components was used. Based on the results, the correlation between resiliency level and happiness of female head was 0.96. The results showed that increasing resiliency in the Female-headed households had a direct and significant effect on their happiness. Based on the results of the high accuracy 0.94 in the final model, fuzzy neural networks can be used well and accurately to predict the level of happiness of Female-headed households, especially at the level of their depression risk.