Detection of Obsessive-Compulsive Disorder in Australian Children and Adolescents Using Machine Learning Methods
Paper
Paper/Presentation Title | Detection of Obsessive-Compulsive Disorder in Australian Children and Adolescents Using Machine Learning Methods |
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Presentation Type | Paper |
Authors | Haque, Umme Maezia (Author), Kabir, Enamul (Author) and Khanam, Rasheda (Author) |
Editors | Traina, Agma, Wang, Hua, Zhang, Yong and Siuly, Siuly |
Journal or Proceedings Title | Lecture Notes in Computer Science (Book series) |
ERA Conference ID | 73271 |
Journal Citation | 13705, pp. 16-25 |
Number of Pages | 10 |
Year | 2022 |
Publisher | Springer |
Place of Publication | Cham, Switzerland |
ISSN | 1611-3349 |
0302-9743 | |
ISBN | 9783031206269 |
9783031206276 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-031-20627-6_2 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-031-20627-6_2 |
Conference/Event | 11th International Conference on Health Information Science (HIS 2022) |
International Conference on Health Information Science (HIS) | |
Event Details | 11th International Conference on Health Information Science (HIS 2022) Event Date 28 to end of 30 Oct 2022 Event Location Virtual |
Event Details | International Conference on Health Information Science (HIS) HIS |
Abstract | Obsessive-compulsive disorder (OCD) is extremely common, but early detection is difficult because symptoms do not appear until puberty. Therefore, it is crucial to identify the causes of this mental illness. Making an early and accurate diagnosis of OCD in children and adolescents is essential to preventing the long-term problems. Several studies have looked at ways to recognise OCD in children, but their accuracy was not very high and they only included a few features and participants. Therefore, the purpose of this study was to examine the detection of OCD utilising machine learning algorithms and 667 features from Young Minds Matter (YMM), Australia’s nationally representative mental health survey of children and adolescents aged 4 to 17 years. According to the internal CV score of the Tree-based Pipeline Optimization Tool (TPOTClassifier), the performance of the suggested technique has been evaluated on the YMM dataset using three of the most optimal algorithms, including Random Forest (RF), Decision Tree (DT), and Gaussian Naïve Bayes (GaussianNB). GaussianNB outperformed all other methods in classifying OCD with 91% accuracy, 76% precision, and 96% specificity, despite significant variation in model performance. |
Keywords | OCD; YMM; RF; DT; GaussianNB; TPOTClassifier |
ANZSRC Field of Research 2020 | 420699. Public health not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Sciences |
School of Mathematics, Physics and Computing | |
Faculty of Business | |
Institution of Origin | University of Southern Queensland |
Edition | LNCS13705 |
https://research.usq.edu.au/item/q7x30/detection-of-obsessive-compulsive-disorder-in-australian-children-and-adolescents-using-machine-learning-methods
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