A Review on Machine Learning Techniques in the Diagnosis of  Psychiatric Disorders

Authors

Mohit Gangwar1 , Sapna Singh2, Rohit Srivastava3, Rohit3 , Chandrabhan Singh3, Neha Goyal4 , Himanshu Kumar Shukla5

Machine Learning, Psychiatric Disorder, Neuroimaging, Magnetic Resonance Imaging.

Diagnosis of psychiatric disorder is intricate clinical entity that could pose challenges for clinicians involving

both accurate identification and effective timely diagnosis. These battles have prompted the evolution of

multiple machine learning approaches to help improve the management of the disorder. These methods use

clinical, anatomical and physiological information and symptoms obtained from neuroimaging and from

clinical investigation to make diagnosis system that may identify psychiatric patients as compared to non

psychiatric patients and predict diagnosis results. This review paper introduces a background on psychiatric

disorder, imaging and machine learning methods. This review paper also discussed about the methodologies

of previous studies which have implemented imaging and machine learning in the diagnosis of psychiatric

disorder and give directions for future use of machine learning techniques in psychiatric-related studies.