Ensemble Hybrid Learning Methods for Automated Depression Detection

Changes in human lifestyle have led to an increase the number of people suffering from depression over past century. Although recent years, rates diagnosing mental illness improved, many cases remain undetected. Automated detection methods can help identify depressed or individuals at risk. An understanding requires effective feature representation and analysis language use. In this article, text classifiers are trained for detection. The key objective is improve performance by examining comparing two sets methods: hybrid ensemble. results show that ensemble models outperform model classification results. strength effectiveness combined features demonstrate better be achieved multiple combinations proper selection.