Abstract
Aims: In the modern era, substance abuse is a global problem. CDC’s (Centre for Disease Control) National Center for Health Statistics reported in July 2021 that, more than 93000 drug overdose deaths occurred alone in the United States. Design: A cohort study. Setting: The drug consumption data is collected online in United Kingdom. Total 2000 records are present, out of which, 1885 records are considered for the work after initial data processing. The dataset has the drug consumption output outcome of 18 drugs, heroin is one of them. Measurements: In this work, an intelligent approach has been proposed for detecting abuse of one of the most illicit substances - heroin. A random forest-based machine learning model is proposed which can predict heroin abused individuals with very high accuracy. For the abuse prediction, various supervised machine learning methods are applied, and their performance is compared. These algorithms are applied to different sets of features. Among the applied five algorithms, feature importance score is calculated for logistic regression, ensemble learning (gradient boosting) and random forest. The feature importance score for each feature is calculated for all applied algorithms k-nearest neighbour and naïve bayes. The ranking of all features is done based on the obtained score. Findings: It is found in the study that the heroin-abuse dataset collected from UCI, two-classification based on random forest (RF) and gradient boosting (GB) achieved more than 90% accuracy as well as more than 90% sensitivity, specificity, precision, and f-score. The proposed model gave the best accuracy 94.697% for heroin-use detection. Conclusions: The assessment of the proposed framework on all possible feature sets shows that the framework works well for heroin abuse prediction as different give satisfactory results.
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References
McCann, U.D., Ricaurte, G.A.: Drug abuse and dependence: hazards and consequences of heroin, cocaine and amphetamines. Curr. Opin. Psychiatry 13(3), 321–325 (2000). https://doi.org/10.1097/00001504-200005000-00014
Paolini, A.C.: Heroin usage: impact on student performance and truancy among high school students. J. Drug Issues 2(1), 853–861 (2016). https://doi.org/10.21767/2471-853X.100011
Morelato, M., Franscella, D., Esseiva, P., Broséus, J.: When does the cutting of cocaine and heroin occur? The first large-scale study based on the chemical analysis of cocaine and heroin seizures in Switzerland. Int. J. Drug Policy 73, 7–15 (2019). https://doi.org/10.1016/j.drugpo.2019.07.025
Becker, T., Papathomas, E., Chan, B.S.: Harm of heroin substitution for cocaine in opioid Naïve patients. Forensic Sci. Addict. Res. 2(4) (2018). https://doi.org/10.31031/FSAR.2018.02.000541
Unick, G., Rosenblum, D., Mars, S., Ciccarone, D.: The relationship between US heroin market dynamics and heroin-related overdose, 1992–2008. Addiction 109(11), 1889–1898 (2014). https://doi.org/10.1111/add.12664
Preston, K.L., Epstein, D.H.: Stress in the daily lives of cocaine and heroin users: relationship to mood, craving, relapse triggers, and cocaine use. Psychopharmacology 218, 29–37 (2011). https://doi.org/10.1007/s00213-011-2183-x
Ismail, M., Stevenson, D., Costa, C., Webb, R., De Puit, M., Bailey, M.: Noninvasive detection of cocaine and heroin use with single fingerprints: determination of an environmental cutoff. Clin. Chem. 64(6), 909–917 (2018). https://doi.org/10.1373/clinchem.2017.281469
Haney, M.: Self-administration of cocaine, cannabis and heroin in the human laboratory: benefits and pitfalls. Addict. Biol. 14(1), 9–21 (2009). https://doi.org/10.1111/j.1369-1600.2008.00121.x
Grassi, M.C., Caricati, A.M., Intraligi, M., Buscema, M., Nencini, P.: Artificial neural network assessment of substitutive pharmacological treatments in hospitalised intravenous drug users. Artif. Intell. Med. 24(1), 37–49 (2002). https://doi.org/10.1016/S0933-3657(01)00093-8
Fehrman, E., Muhammad, A.K., Mirkes, E.M., Egan, V., Gorban, A.N.: The five factor model of personality and evaluation of drug consumption risk. In: Palumbo, F., Montanari, A., Vichi, M. (eds.) Data Science. Studies in Classification, Data Analysis, and Knowledge Organization, pp. 231–242. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55723-6_18
Ahn, W.Y., Vassileva, J.: Machine-learning identifies substance-specific behavioral markers for opiate and stimulant dependence. Drug Alcohol Depend. 1(161), 247–257 (2016). https://doi.org/10.1016/j.drugalcdep.2016.02.008
Mak, K.K., Lee, K., Park, C.: Applications of machine learning in addiction studies: a systematic review. Psychiatry Res. 1(275), 53–60 (2019). https://doi.org/10.1016/j.psychres.2019.03.001
Fehrman, E., Muhammad, A.K., Mirkes, E., Egan, V., Gorban, A.: Drug Consumption (quantified) Data Set (2016). [WWW Document]. https://archive.ics.uci.edu/ml/datasets/Drug+consumption+%28quantified%29
Linting, M., van der Kooij, A.: Nonlinear principal components analysis with CATPCA: a tutorial. J. Pers. Assess. 94(1), 12–25 (2012). https://doi.org/10.1080/00223891.2011.627965
Fernández, A., Garcia, S., Herrera, F., Chawla, N.V.: SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. J. Artif. Intell. Res. 61, 863–905 (2018). https://doi.org/10.1613/jair.1.11192
Zeng, M., Zou, B., Wei, F., Liu, X., Wang, L.: Effective prediction of three common diseases by combining SMOTE with Tomek links technique for imbalanced medical data. In: 2016 IEEE International Conference of Online Analysis and Computing Science (ICOACS), pp. 225–228, IEEE, 28 May 2016. https://doi.org/10.1109/ICOACS.2016.7563084
Hastie, T., Tibshirani, R., Friedman, J.H., Friedman, J.H.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York (2009). https://doi.org/10.1007/978-0-387-84858-7
Peterson, L.E.: K-nearest neighbor. Scholarpedia 4(2), 1883 (2009)
Kumari, D., Kilam, S., Nath, P., Swetapadma, A.: Prediction of alcohol abused individuals using artificial neural network. Int. J. Inf. Technol. 10, 233–237 (2018)
.Kumari, D., Swetapadma, A.: A novel method for predicting time of alcohol use based on personality traits and demographic information. IETE J. Res. 1–10 (2022)
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Kumari, D., Swetapadma, A. (2024). Building a Heroin Abuse Prediction Model: A Generalized Machine Learning Approach. In: Santosh, K., et al. Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2023. Communications in Computer and Information Science, vol 2026. Springer, Cham. https://doi.org/10.1007/978-3-031-53082-1_1
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