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Predicting Individual Substance Abuse Vulnerability Using Machine Learning Techniques

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Hybrid Intelligent Systems (HIS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1375))

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Abstract

Substance abuse is the unrestrained and detrimental use of psychoactive chemical substances, unauthorized drugs, and alcohol. Continuous use of these substances can ultimately lead a human to disastrous consequences. As patients display a high rate of relapse, prevention at an early stage can be an effective restraint. We therefore propose a binary classifier to identify any individual’s present vulnerability towards substance abuse by analyzing subjects’ socio-economic environment. We have collected data by a questionnaire which is created after carefully assessing the commonly involved factors behind substance abuse. Pearson’s chi-squared test of independence is used to identify key feature variables influencing substance abuse. Later we build the predictive classifiers using machine learning classification algorithms on those variables. Logistic regression classifier trained with 18 features can predict individual vulnerability with the best accuracy.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chittagong, 4349, Bangladesh

    Uwaise Ibna Islam, Iqbal H. Sarker & Mohammed Moshiul Hoque

  2. McMaster University, Hamilton, Canada

    Enamul Haque

Authors
  1. Uwaise Ibna Islam
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  2. Iqbal H. Sarker
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  3. Enamul Haque
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  4. Mohammed Moshiul Hoque
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Corresponding author

Correspondence to Iqbal H. Sarker .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Institute for Information Systems, University of Applied Sciences and Arts Northwestern Switzerland, Olten, Solothurn, Switzerland

    Thomas Hanne

  3. Division of Graduate Studies, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, AL, USA

    Niketa Gandhi

  5. Universidade Federal da Bahia, Salvador, Bahia, Brazil

    Tatiane Nogueira Rios

  6. Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

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Islam, U.I., Sarker, I.H., Haque, E., Hoque, M.M. (2021). Predicting Individual Substance Abuse Vulnerability Using Machine Learning Techniques. In: Abraham, A., Hanne, T., Castillo, O., Gandhi, N., Nogueira Rios, T., Hong, TP. (eds) Hybrid Intelligent Systems. HIS 2020. Advances in Intelligent Systems and Computing, vol 1375. Springer, Cham. https://doi.org/10.1007/978-3-030-73050-5_42

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