Rule generation and prediction of Anxiety Disorder using Logistic Model Trees

Anxiety disorder is a type of mental illness characterized by excessive worrying, uneasiness and fear about future uncertainties which may affect psychological and physical health. It is caused due to area of the brain i.e. amygdala, that control fear responses. Amongst the available treatments for the anxiety disorder, cognitive behavioral therapy is a best treatment where patient's lifestyle, their habits, their environment etc. is studied by psychiatrist and related consultancy is given to them. For studying the patient lifestyle, data mining is a solution through which contextual data can be gathered from the patient and stress concerning rules can be generated. It can also help medical practitioner to find out actual reason affecting the mental health of the patient and give appropriate treatment to their patient. The main aim of this research is to provide a new approach for generating the relevant stress concerning rules and to predict the stress level of a patient using logistic model trees to help medical society.