DIFFERENT APPROACHES IN DETECTING ANXIETY DISORDERS AMONG POTENTIAL PATIENTS THROUGH DEEP LEARNING AND AI
Keywords:
Medical imaging, Deep learning, AI, Anxiety disorders, Autoencoders, Medical image datasets, Annotation, Segmentation, Image storage, medical image analysis, neuroimaging, Psychiatric diagnosis Social Anxiety Disorder, Random Forest Classifier, SVM, Generalized Anxiety Disorder, posttraumatic stress disorder, agoraphobia, and a history of suicideAbstract
A form of the mental condition known as anxiety disorder is characterized by intense feelings of fear and worry. Tools that help doctors forecast mental diseases and provide better patient treatment have greatly benefited in the last few years from the development of ML approaches. This study's comparative literature review focused on the machine learning prediction of certain anxiety disorders and suicidal propensity. A literature search is conducted on studies released between 2014 and May 2023. A comparative literature evaluation included studies on the application of ML approaches to the prediction of anxiety disorders. Analysis of 20 research showed that ML models may be utilized to forecast anxiety disorders, while analysis of two studies might be used to forecast suicidal inclinations. The accuracy of the outcome changes depending on the type of anxiety problem and the techniques employed to forecast it.
