Predicting Co-Occurring Mental Health and Substance Use Disorders in Women: An Automated Machine Learning Approach
Article
Acharya, Nirmal, Kar, Padmaja, Ally, Mustafa and Soar, Jeffrey. 2024. "Predicting Co-Occurring Mental Health and Substance Use Disorders in Women: An Automated Machine Learning Approach." Applied Sciences. 14 (4). https://doi.org/10.3390/app14041630
| Article Title | Predicting Co-Occurring Mental Health and Substance Use Disorders in Women: An Automated Machine Learning Approach |
|---|---|
| ERA Journal ID | 211776 |
| Article Category | Article |
| Authors | Acharya, Nirmal, Kar, Padmaja, Ally, Mustafa and Soar, Jeffrey |
| Journal Title | Applied Sciences |
| Journal Citation | 14 (4) |
| Article Number | 1630 |
| Number of Pages | 13 |
| Year | 2024 |
| Publisher | MDPI AG |
| ISSN | 2076-3417 |
| Digital Object Identifier (DOI) | https://doi.org/10.3390/app14041630 |
| Web Address (URL) | https://www.mdpi.com/2076-3417/14/4/1630 |
| Abstract | Significant clinical overlap exists between mental health and substance use disorders, especially among women. The purpose of this research is to leverage an AutoML (Automated Machine Learning) interface to predict and distinguish co-occurring mental health (MH) and substance use disorders (SUD) among women. By employing various modeling algorithms for binary classification, including Random Forest, Gradient Boosted Trees, XGBoost, Extra Trees, SGD, Deep Neural Network, Single-Layer Perceptron, K Nearest Neighbors (grid), and a super learning model (constructed by combining the predictions of a Random Forest model and an XGBoost model), the research aims to provide healthcare practitioners with a powerful tool for earlier identification, intervention, and personalised support for women at risk. The present research presents a machine learning (ML) methodology for more accurately predicting the co-occurrence of mental health (MH) and substance use disorders (SUD) in women, utilising the Treatment Episode Data Set Admissions (TEDS-A) from the year 2020 (n = 497,175). A super learning model was constructed by combining the predictions of a Random Forest model and an XGBoost model. The model demonstrated promising predictive performance in predicting co-occurring MH and SUD in women with an AUC = 0.817, Accuracy = 0.751, Precision = 0.743, Recall = 0.926 and F1 Score = 0.825. The use of accurate prediction models can substantially facilitate the prompt identification and implementation of intervention strategies. |
| Keywords | mental health; substance use disorder; AutoML; machine learning |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 460999. Information systems not elsewhere classified |
| Byline Affiliations | Australian International Institute of Higher Education, Australia |
| St Vincent’s Care Services, Australia | |
| School of Business |
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https://research.usq.edu.au/item/z5q79/predicting-co-occurring-mental-health-and-substance-use-disorders-in-women-an-automated-machine-learning-approach
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