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Classical and Neural Network Machine Learning to Determine the Risk of Marijuana Use

Author

Listed:
  • Laura Zoboroski

    (Data Analytics Certificate Program, Graduate School of Engineering and Management, Air Force Institute of Technology, Wright-Patterson AFB, Dayton, OH 45433, USA)

  • Torrey Wagner

    (Data Analytics Certificate Program, Graduate School of Engineering and Management, Air Force Institute of Technology, Wright-Patterson AFB, Dayton, OH 45433, USA
    The Perduco Group (a LinQuest Company), Dayton, OH 45433, USA)

  • Brent Langhals

    (Data Analytics Certificate Program, Graduate School of Engineering and Management, Air Force Institute of Technology, Wright-Patterson AFB, Dayton, OH 45433, USA)

Abstract

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.

Suggested Citation

  • Laura Zoboroski & Torrey Wagner & Brent Langhals, 2021. "Classical and Neural Network Machine Learning to Determine the Risk of Marijuana Use," IJERPH, MDPI, vol. 18(14), pages 1-15, July.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:14:p:7466-:d:593448
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    References listed on IDEAS

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    1. Feng-Hsu Wang & Chih-Ming Lin, 2020. "The Utility of Artificial Neural Networks for the Non-Invasive Prediction of Metabolic Syndrome Based on Personal Characteristics," IJERPH, MDPI, vol. 17(24), pages 1-10, December.
    2. Sangwon Chae & Sungjun Kwon & Donghyun Lee, 2018. "Predicting Infectious Disease Using Deep Learning and Big Data," IJERPH, MDPI, vol. 15(8), pages 1-20, July.
    3. Wen-Yu Ou Yang & Cheng-Chien Lai & Meng-Ting Tsou & Lee-Ching Hwang, 2021. "Development of Machine Learning Models for Prediction of Osteoporosis from Clinical Health Examination Data," IJERPH, MDPI, vol. 18(14), pages 1-12, July.
    4. Cheng-Chien Lai & Wei-Hsin Huang & Betty Chia-Chen Chang & Lee-Ching Hwang, 2021. "Development of Machine Learning Models for Prediction of Smoking Cessation Outcome," IJERPH, MDPI, vol. 18(5), pages 1-10, March.
    Full references (including those not matched with items on IDEAS)

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