Ensemble Hybrid Learning Methods for Automated Depression Detection

Loading...
Thumbnail Image
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2023-02-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
9
211-219
Series
IEEE Transactions on Computational Social Systems, Volume 10, issue 1
Abstract
Changes in human lifestyle have led to an increase in the number of people suffering from depression over the past century. Although in recent years, rates of diagnosing mental illness have improved, many cases remain undetected. Automated detection methods can help identify depressed or individuals at risk. An understanding of depression detection requires effective feature representation and analysis of language use. In this article, text classifiers are trained for depression detection. The key objective is to improve depression detection performance by examining and comparing two sets of methods: hybrid and ensemble. The results show that ensemble models outperform the hybrid model classification results. The strength and effectiveness of the combined features demonstrate that better performance can be achieved by multiple feature combinations and proper feature selection.
Description
Publisher Copyright: Author
Keywords
Data models, Deep neural networks, Depression, depression detection, ensemble methods, Feature extraction, Hidden Markov models, Linguistics, Neural networks, sentiment lexicon., Social networking (online)
Other note
Citation
Ansari, L, Ji, S, Chen, Q & Cambria, E 2023, ' Ensemble Hybrid Learning Methods for Automated Depression Detection ', IEEE Transactions on Computational Social Systems, vol. 10, no. 1, pp. 211-219 . https://doi.org/10.1109/TCSS.2022.3154442