Predicting mindfulness effect on irritability with Bayesian models, regression and neural network

Artificial intelligence researchers are trying to implement human intelligence on the machine. This study aimed to develop an appropriate predictive computer model to evaluate the effectiveness of mindfulness-based cognitive therapy on irritability.

The design of the present study is quasi-experimental with a pre-test and post-test method. 135 individuals who referred to Khane Mehr counseling center in Mashhad and participated in an 8-session mindfulness-based cognitive therapy (MBCT) course were included in this study. Totally, 11 MBCT courses were held and 10 to 14 people participated in each course. Participants completed the irritability questionnaire (Pourafrouz & et al.) at two stages (before treatment and after treatment). In order to examine the differences from pre-test to post-test in this research, the variance analysis of repeated measures was used.

There was a significant difference between pre-test and post-test irritability scores. The effect of mindfulness was 83%. To develop the prediction model, three Bayesian, regression, and neural network models were compared. The Bayesian model, with 93% accuracy test data, was considered the most appropriate model. Moreover, the Bayesian models with input and output clustering (85.7%), the Bayesian with classification (71.49%), and the sequential neural network (64.29%) were identified as suitable models to predict the effectiveness of 8-session mindfulness courses on reducing irritability. The Bayesian model with output clustering, one-output regression, and the Convulsions Neural Network did not have sufficient predictive accuracy for the effectiveness of mindfulness.

Using cognitive modeling, we can predict the efficacy of mindfulness-based cognitive therapy on irritability.