ABSTRACT
Depression is the most common psychiatric disorder worldwide, which affects more than 300 million people. We aimed to detect depressed patients and healthy people automatically. We work on the PHQ-9 questionnaires and reduced it to a PHQ-5 questionnaires with a new cut-off value of 8 to detect depressed patients. We trained a Neural Network with 70% of our dataset. Then, the proposed classifier was tested with two datasets. The first one consists of 30% of PHQ-5 datasets, which could achieve 85.69%, 99.11% and 90.56% for accuracy, sensitivity and specificity respectively. The second test dataset consists of physical patient's parameters which recorded during a study in the Hanover Medical School. This classifier has shown good results in the detection of depression based on these two datasets.
