Application of Machine learning algorithms in diagnosis and detection of psychological disorders

Abstract

A psychological disorder can be described as the disturbance of the natural state of the mind that affects the cognitive and social behaviour of the individual. The rapid modernization of society and the lack of social and personal interactions are further assisting in the increasing number new cases of psychological disorders. This paper intends to provides a brief overview of existing research being carried out in the field of machine learning and diagnosis, classification and prediction of psychological disorders and will present a sample framework which uses the data from the electronic health records to extract different text-based documents and reports to produce a tagged list of words relevant to disorder which is matched against the symptoms and signs of different psychological disorders to predict the disorder. To validate this prediction, it is further checked against the output of the machine learning models that will predict the psychological disorder based on the patient’s fMRI image and PET images extracted from the patient’s EHR. Through this paper, readers will be able to get an overview of the recent developments in the field of diagnosis of mental disorders by utilizing the machine learning algorithms and techniques to process the relevant unstructured data for improving the accuracy of the diagnosis to reduce the risk of misdiagnosis.