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Classical and Neural Network Machine Learning to Determine the Risk of Marijuana Use
Marijuana is the most commonly abused drug for military personnel tested at the Air Force Drug Testing Laboratory. A publicly available dataset of drug use, personality trait scores and demographic data was modeled with logistic regression, decision tree and neural network models to determine the ex...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8304402/ https://www.ncbi.nlm.nih.gov/pubmed/34299915 http://dx.doi.org/10.3390/ijerph18147466 |
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author | Zoboroski, Laura Wagner, Torrey Langhals, Brent |
author_facet | Zoboroski, Laura Wagner, Torrey Langhals, Brent |
author_sort | Zoboroski, Laura |
collection | PubMed |
description | Marijuana is the most commonly abused drug for military personnel tested at the Air Force Drug Testing Laboratory. A publicly available dataset of drug use, personality trait scores and demographic data was modeled with logistic regression, decision tree and neural network models to determine the extent to which marijuana use can be predicted using personality traits. While the logistic regression model had lower performance than the neural network model, it matched the sensitivity of prior work (0.80), achieved a high level of significance (p < 0.05) and yielded valuable inferences. It implied that younger, less educated individuals who exhibit sensation-seeking behavior and are open to experience tend to be at higher risk for THC use. A method for performing an iterative multidimensional neural network hyperparameter search is presented, and two iterations of a 6-dimensional search were performed. Metrics were used to select a family of 8 promising models from a cohort of 4600 models, and the best NN model’s 0.87 sensitivity improved upon the literature. The model met an f1 overfitting threshold on the test and holdout datasets, and an accuracy sensitivity analysis on a holdout-equivalent dataset yielded a 95% CI of 0.86 ± 0.04. These results have the potential to increase the efficacy of drug prevention and intervention programs. |
format | Online Article Text |
id | pubmed-8304402 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83044022021-07-25 Classical and Neural Network Machine Learning to Determine the Risk of Marijuana Use Zoboroski, Laura Wagner, Torrey Langhals, Brent Int J Environ Res Public Health Article Marijuana is the most commonly abused drug for military personnel tested at the Air Force Drug Testing Laboratory. A publicly available dataset of drug use, personality trait scores and demographic data was modeled with logistic regression, decision tree and neural network models to determine the extent to which marijuana use can be predicted using personality traits. While the logistic regression model had lower performance than the neural network model, it matched the sensitivity of prior work (0.80), achieved a high level of significance (p < 0.05) and yielded valuable inferences. It implied that younger, less educated individuals who exhibit sensation-seeking behavior and are open to experience tend to be at higher risk for THC use. A method for performing an iterative multidimensional neural network hyperparameter search is presented, and two iterations of a 6-dimensional search were performed. Metrics were used to select a family of 8 promising models from a cohort of 4600 models, and the best NN model’s 0.87 sensitivity improved upon the literature. The model met an f1 overfitting threshold on the test and holdout datasets, and an accuracy sensitivity analysis on a holdout-equivalent dataset yielded a 95% CI of 0.86 ± 0.04. These results have the potential to increase the efficacy of drug prevention and intervention programs. MDPI 2021-07-13 /pmc/articles/PMC8304402/ /pubmed/34299915 http://dx.doi.org/10.3390/ijerph18147466 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zoboroski, Laura Wagner, Torrey Langhals, Brent Classical and Neural Network Machine Learning to Determine the Risk of Marijuana Use |
title | Classical and Neural Network Machine Learning to Determine the Risk of Marijuana Use |
title_full | Classical and Neural Network Machine Learning to Determine the Risk of Marijuana Use |
title_fullStr | Classical and Neural Network Machine Learning to Determine the Risk of Marijuana Use |
title_full_unstemmed | Classical and Neural Network Machine Learning to Determine the Risk of Marijuana Use |
title_short | Classical and Neural Network Machine Learning to Determine the Risk of Marijuana Use |
title_sort | classical and neural network machine learning to determine the risk of marijuana use |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8304402/ https://www.ncbi.nlm.nih.gov/pubmed/34299915 http://dx.doi.org/10.3390/ijerph18147466 |
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