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A Machine Learning Model for Predicting Individual Substance Abuse with Associated Risk-Factors

Author

Listed:
  • Uwaise Ibna Islam

    (Chittagong University of Engineering & Technology)

  • Enamul Haque

    (University of Waterloo)

  • Dheyaaldin Alsalman

    (Dar Al-Hekma University)

  • Muhammad Nazrul Islam

    (Military Institute of Science and Technology)

  • Mohammad Ali Moni

    (The University of Queensland)

  • Iqbal H. Sarker

    (Chittagong University of Engineering & Technology)

Abstract

Substance abuse is the unrestrained and detrimental use of psychoactive chemical substances, unauthorized drugs, and alcohol that can ultimately lead a human to disastrous consequences. As patients with this behavior display a high value of relapse, the best intervention approach is to prevent it at the very beginning. In this paper, we propose a framework based on machine learning techniques to identify individual vulnerability towards substance abuse by analyzing socio-economic aspects. We have carefully assessed the commonly involved causes to form the questionnaire for collecting data from healthy people and patients suffering from substance abuse. Using Pearson’s chi-squared test of independence, feature importance is measured to eliminate less significant features using backward elimination. Popular machine learning classification algorithms (Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, K-Nearest neighbors, and Gaussian Naive Bayes) are used to build the predictive classifier. To identify the key risk-factors of individual substance abuse, we extract association rules from the significant features and subsequent factors. Experimental results on real data-set support the effectiveness of the proposed framework.

Suggested Citation

  • Uwaise Ibna Islam & Enamul Haque & Dheyaaldin Alsalman & Muhammad Nazrul Islam & Mohammad Ali Moni & Iqbal H. Sarker, 2023. "A Machine Learning Model for Predicting Individual Substance Abuse with Associated Risk-Factors," Annals of Data Science, Springer, vol. 10(6), pages 1607-1634, December.
    • Handle: RePEc:spr:aodasc:v:10:y:2023:i:6:d:10.1007_s40745-022-00381-0
    DOI: 10.1007/s40745-022-00381-0
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    References listed on IDEAS

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    1. Devansh Patel & Dhwanil Shah & Manan Shah, 2020. "The Intertwine of Brain and Body: A Quantitative Analysis on How Big Data Influences the System of Sports," Annals of Data Science, Springer, vol. 7(1), pages 1-16, March.
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