Predicting Co-Occurring Mental Health and Substance Use Disorders in Women: An Automated Machine Learning Approach
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) amo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/4/1630 |
| _version_ | 1797298993075585024 |
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| author | Nirmal Acharya Padmaja Kar Mustafa Ally Jeffrey Soar |
| author_facet | Nirmal Acharya Padmaja Kar Mustafa Ally Jeffrey Soar |
| author_sort | Nirmal Acharya |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T22:44:04Z |
| format | Article |
| id | doaj.art-eba229a632e94580880b006cca3f0e71 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-07T22:44:04Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-eba229a632e94580880b006cca3f0e712024-02-23T15:06:36ZengMDPI AGApplied Sciences2076-34172024-02-01144163010.3390/app14041630Predicting Co-Occurring Mental Health and Substance Use Disorders in Women: An Automated Machine Learning ApproachNirmal Acharya0Padmaja Kar1Mustafa Ally2Jeffrey Soar3Australian International Institute of Higher Education, Brisbane, QLD 4000, AustraliaSt Vincent’s Care Services, Mitchelton, QLD 4053, AustraliaSchool of Business, University of Southern Queensland, Toowoomba, QLD 4350, AustraliaSchool of Business, University of Southern Queensland, Toowoomba, QLD 4350, AustraliaSignificant 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.https://www.mdpi.com/2076-3417/14/4/1630mental healthsubstance use disordermachine learningAutoML |
| spellingShingle | Nirmal Acharya Padmaja Kar Mustafa Ally Jeffrey Soar Predicting Co-Occurring Mental Health and Substance Use Disorders in Women: An Automated Machine Learning Approach Applied Sciences mental health substance use disorder machine learning AutoML |
| title | Predicting Co-Occurring Mental Health and Substance Use Disorders in Women: An Automated Machine Learning Approach |
| title_full | Predicting Co-Occurring Mental Health and Substance Use Disorders in Women: An Automated Machine Learning Approach |
| title_fullStr | Predicting Co-Occurring Mental Health and Substance Use Disorders in Women: An Automated Machine Learning Approach |
| title_full_unstemmed | Predicting Co-Occurring Mental Health and Substance Use Disorders in Women: An Automated Machine Learning Approach |
| title_short | Predicting Co-Occurring Mental Health and Substance Use Disorders in Women: An Automated Machine Learning Approach |
| title_sort | predicting co occurring mental health and substance use disorders in women an automated machine learning approach |
| topic | mental health substance use disorder machine learning AutoML |
| url | https://www.mdpi.com/2076-3417/14/4/1630 |
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