Detecting depression using an ensemble classifier based on Quality of Life scales
Article
Article Title | Detecting depression using an ensemble classifier based on Quality of Life scales |
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ERA Journal ID | 211938 |
Article Category | Article |
Authors | Tao, Xiaohui (Author), Chi, Oliver (Author), Delaney, Patrick J. (Author), Li, Lin (Author) and Huang, Jiajin (Author) |
Journal Title | Brain Informatics |
Journal Citation | 8, pp. 1-15 |
Article Number | 2 |
Number of Pages | 15 |
Year | 2021 |
Publisher | Springer |
Place of Publication | Switzerland |
ISSN | 2198-4018 |
2198-4026 | |
Digital Object Identifier (DOI) | https://doi.org/10.1186/s40708-021-00125-5 |
Web Address (URL) | https://braininformatics.springeropen.com/articles/10.1186/s40708-021-00125-5 |
Abstract | Major depressive disorder (MDD) is an issue that affects 350 million people worldwide. Traditional approaches have been to identify depressive symptoms in datasets, but recently, research is beginning to explore the association between psychosocial factors such as those on the quality of life scale and mental well-being, which will lead to earlier diagnosis and prediction of MDD. In this research, an ensemble binary classifier is proposed to analyse health survey data against ground truth from the SF-20 Quality of Life scales. The classifier aims to improve the performance of machine learning techniques on large datasets and identify depressed cases based on associations between items on the QoL scale and mental illness by increasing predictive performance. On the experimental evaluation on the National Health and Nutrition Examination Survey (NHANES), the classifier demonstrated an F1 score of 0.976 in the prediction, without any incorrectly identified depression instances. Only about 4% of instances had been mistakenly classified into depressed cases, with a significant accuracy of 95.4% comparing to the result from PHQ-9 mental screen inventory. The presented ensemble binary classifier performed comparably better than each baseline algorithm in all measures and all experiments. We trained the ensemble model on the processed NHANES dataset, tested and evaluated the results of its performance against mental screen inventory and discussed the comparable predictions. Finally, we provided future research directions. |
Keywords | Major depressive disorder, Ensemble classification, Supervised machine learning |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 420313. Mental health services |
460899. Human-centred computing not elsewhere classified | |
460502. Data mining and knowledge discovery | |
Byline Affiliations | School of Sciences |
University of Technology Sydney | |
Wuhan University of Technology, China | |
Beijing University of Technology, China | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q63yv/detecting-depression-using-an-ensemble-classifier-based-on-quality-of-life-scales
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