Comparison of the Accuracy of Drug User Classification Models Using Machine Learning Methods

Nursela Basuni; Amril Mutoi Siregar

doi:10.29207/resti.v7i6.5401

Nursela Basuni Universitas Buana Perjuangan Karawang
Amril Mutoi Siregar Universitas Buana Perjuangan Karawang

DOI: https://doi.org/10.29207/resti.v7i6.5401

Keywords: drug consumption, classification, machine learning, confusion matrix, AUC curve

Abstract

Drug abuse are on the rise, with many users enter the addiction phase, often resulting in overdose and death. Drugs are chemical compounds that are capable of affecting biological functions, and they can induce feelings of happiness and reduce pain. To address this growing problem, a proactive measure is needed. Therefore, this study aims to classify drug users and non-users, so that health workers and therapists can educate about the dangers of drugs to non-users and rehabilitate drug users. This study uses drug consumption data taken from the UCI Irvine Machine Learning Repository. The data consist of 1885 rows with 32 attributes and 2 classes, where there are 18 types of legal and illegal drugs. This research utilizes machine learning methods, specifically Artificial Neural Networks (ANN), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF), in addition to evaluation methods such as Confusion Matrix and Area Under Curve (AUC). The results showed that RF outperformed the other methods, with accuracy, precision, and recall of 93%, and an f1 score of 89%, while the AUC value was still suboptimal at 0.66. DT had the worst results, with 82% precision, 87% precision, 82% recall, 84% f1 score, and an AUC value of 0.56. With these results, this research can be continued into an application that can classify drug users and nonusers.

Downloads

Download data is not yet available.

References

J. Feng Liu and J. Xu Li, “Drug addiction: a curable mental disorder?” Acta Pharmacologica Sinica, vol. 39, no. 12. Nature Publishing Group, pp. 1823–1829, Dec. 01, 2018. doi: 10.1038/s41401-018-0180-x.

F. Zhao et al., “Computational Approaches to Detect Illicit Drug Ads and Find Vendor Communities Within Social Media Platforms,” IEEE/ACM Trans Comput Biol Bioinform, vol. 19, no. 1, pp. 180–191, 2022, doi: 10.1109/TCBB.2020.2978476.

A. Islam, M. Sadekur Rahman, M. Tarek Habib, M. Ariful Islam Arif, S. Islam Sany, and F. Sharmin, “Prediction of Addiction to Drugs and Alcohol Using Machine Learning: A Case Study on Bangladeshi Population E+ Youth (Galvanizing Energy with Experience towards Youth Empowerment) View project machine learning View project Prediction of addiction to drugs and alcohol using machine learning: A case study on Bangladeshi population,” Article in International Journal of Electrical and Computer Engineering, vol. 11, no. 5, pp. 4471–4480, 2021, doi: 10.11591/ijece.v11i5.

G. Shobana and S. N. Bushra, “Drug administration route classification using machine learning models,” in Proceedings of the 3rd International Conference on Intelligent Sustainable Systems, ICISS 2020, Institute of Electrical and Electronics Engineers Inc., Dec. 2020, pp. 654–659. doi: 10.1109/ICISS49785.2020.9315975.

F. P. Brishty and S. Akhter, “Detection of drug-induced QT Syndrome from ECG using machine learning techniques,” Dec. 2018.

G. Shobana and N. Priya, “Cancer drug classification using artificial neural network with feature selection,” in Proceedings of the 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021, Institute of Electrical and Electronics Engineers Inc., Feb. 2021, pp. 1250–1255. doi: 10.1109/ICICV50876.2021.9388542.

L. Mandal and N. D. Jana, “A Comparative Study of Naive Bayes and k-NN Algorithm for Multi-class Drug Molecule Classification,” A Comparative Study of Naive Bayes and k-NN Algorithm for Multi-class Drug Molecule Classification, 2019.

V. Da Poian et al., “Exploratory data analysis (EDA) machine learning approaches for ocean world analog mass spectrometry,” Frontiers in Astronomy and Space Sciences, vol. 10, 2023, doi: 10.3389/fspas.2023.1134141.

T. Milo and A. Somech, “Automating Exploratory Data Analysis via Machine Learning: An Overview,” in Proceedings of the ACM SIGMOD International Conference on Management of Data, Association for Computing Machinery, Jun. 2020, pp. 2617–2622. doi: 10.1145/3318464.3383126.

R. Indrakumari, T. Poongodi, and S. R. Jena, “Heart Disease Prediction using Exploratory Data Analysis,” in Procedia Computer Science, Elsevier B.V., 2020, pp. 130–139. doi: 10.1016/j.procs.2020.06.017.

A. Shah et al., “A comprehensive study on skin cancer detection using artificial neural network (ANN) and convolutional neural network (CNN),” Clinical eHealth, vol. 6, pp. 76–84, Dec. 2023, doi: 10.1016/j.ceh.2023.08.002.

B. P. Adedeji, “Electric vehicles survey and a multifunctional artificial neural network for predicting energy consumption in all-electric vehicles,” Results in Engineering, vol. 19, Sep. 2023, doi: 10.1016/j.rineng.2023.101283.

Q. Ren, H. Zhang, D. Zhang, X. Zhao, L. Yan, and J. Rui, “A novel hybrid method of lithology identification based on k-means++ algorithm and fuzzy decision tree,” J Pet Sci Eng, vol. 208, Jan. 2022, doi: 10.1016/j.petrol.2021.109681.

M. Bansal, A. Goyal, and A. Choudhary, “A comparative analysis of K-Nearest Neighbor, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory algorithms in machine learning,” Decision Analytics Journal, vol. 3, p. 100071, Jun. 2022, doi: 10.1016/j.dajour.2022.100071.

R. Prabaswara, J. Lemantara, and J. Jusak, “Classification of Secondary School Destination for Inclusive Students using Decision Tree Algorithm,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 7, no. 5, Aug. 2023, doi: 10.29207/resti.v7i5.5081.

Z. Xu, J. Cao, G. Zhang, X. Chen, and Y. Wu, “Active learning accelerated Monte-Carlo simulation based on the modified K-nearest neighbors algorithm and its application to reliability estimations,” Defence Technology, 2022, doi 10.1016/j.dt.2022.09.012.

D. Cheng, Y. Shi, T. Lin, B. H. Gwee, and K. A. Toh, “Hybrid K-means clustering and support vector machine method for via and metal line detections in delayered IC images,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 65, no. 12, pp. 1849–1853, Dec. 2018, doi: 10.1109/TCSII.2018.2827044.

R. Yao, J. Li, M. Hui, L. Bai, and Q. Wu, “Feature Selection Based on Random Forest for Partial Discharges Characteristic Set,” IEEE Access, vol. 8, pp. 159151–159161, 2020, doi: 10.1109/ACCESS.2020.3019377.

H. Yun, “Prediction model of algal blooms using logistic regression and confusion matrix,” International Journal of Electrical and Computer Engineering, vol. 11, no. 3, pp. 2407–2413, Jun. 2021, doi: 10.11591/ijece.v11i3.pp2407-2413.

D. Valero-Carreras, J. Alcaraz, and M. Landete, “Comparing two SVM models through different metrics based on the confusion matrix,” Comput Oper Res, vol. 152, Apr. 2023, doi: 10.1016/j.cor.2022.106131.

A. Nugroho, A. Bimo Gumelar, A. G. Sooai, D. Sarvasti, and P. L. Tahalele, “Perbandingan Performansi Algoritma Pengklasifikasian Terpandu Untuk Kasus Penyakit Kardiovaskular,” masa berlaku mulai, vol. 1, no. 3, pp. 998–1006, 2017.

M. H. Z. Al Faroby, M. I. Irawan, and N. N. T. Puspaningsih, “XGBoost and Network Analysis for Prediction of Proteins Affecting Insulin based on Protein-Protein Interactions,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, pp. 253–262, Nov. 2020, doi: 10.22219/kinetik.v5i4.1076.

S. Narkhede, “Understanding AUC - ROC Curve,” Understanding AUC - ROC Curve, Jun. 2018.

A. J. Bowers and X. Zhou, “Receiver Operating Characteristic (ROC) Area Under the Curve (AUC): A Diagnostic Measure for Evaluating the Accuracy of Predictors of Education Outcomes,” J Educ Stud Placed Risk, vol. 24, no. 1, pp. 20–46, Jan. 2019, doi: 10.1080/10824669.2018.1523734.