Artificial Neural Networks in Drug Addiction Diagnosis

Abstract

This study aims to find a simple mechanism to help researchers and families identify addicts. In this paper, the Artificial Neural Network (ANN) method has been examined to determine whether a person is an addict. In this study, the dataset obtained from students from different countries and published as open source by Atif Masih was used. This dataset contains 50343 samples with 11 features. The study involved testing and comparing multiple neural network architectures based on their average classification accuracy. When the correlation matrix is examined, it is seen that the relationships between the variables are almost negligible. This can be attributed to the fact that the variables are categorical. Each architecture was trained using 10 different seed numbers, and the mean accuracy was calculated accordingly. The experiment results have obtained 75.53% classification accuracy for correct diagnosis in our system. Our model could significantly expedite the diagnosis and treatment of addiction, providing a valuable tool for families, physicians, and investigators. The paper proposes a Decision Support System (DSS) for diagnosing addiction, leveraging one of the most widely-used machine learning techniques: Artificial Neural Networks (ANN).

Keywords

• Drug addiction
• Artificial neural network
• Decision support system

References

1. Afzali MH, Sunderland M, Stewart S, Masse B, Seguin J, Newton N, Teesson M, Conrod P. 2019. Machine-learning prediction of adolescent alcohol use: A cross-study, cross-cultural validation. Addiction, 114(4): 662-671.
2. Araghinejad S. 2014. Data-Driven modeling: Using MATLAB® in water resources and environmental engineering. Springer, Netherlands, pp: 67.
3. Bottou L. 2014. From machine learning to machine reasoning: An essay. Machine Learn, 94: 133-149.
4. Fausett LV. 2006. Fundamentals of neural networks: architectures, algorithms and applications. Pearson Education India, pp: 461.
5. Gupta P. 2019. Top management team heterogeneity, corporate social responsibility disclosure and financial performance. Am J Ind Bus Manag, 9: 1076-1093.
6. Islam Arif MA, Sany SI, Sharmin F, Rahman MS, Habib MT. 2021. Prediction of addiction to drugs and alcohol using machine learning: A case study on Bangladeshi population. Int J Electr Comput Eng, 11(5): 4471.
7. Johnson JM, Khoshgoftaar TM. 2019. Survey on deep learning with class imbalance. J Big Data, 6(1): 1-54.
8. Junghare A, Milani K, Chavan M, Ransing V. 2019. Application for drug addicts using artificial neural networks. In: Proc Int Conf Commun Inf Process (ICCIP), Mumbai, India, pp:15-25.

9. Kaggle. 2024. Students drugs Addiction Dataset. URL: https://www.kaggle.com/datasets/atifmasih/students-drugs-addiction-dataset (accessed date: May 24, 2024).
10. Kumari D, Kilam S, Nath P, Swetapadma A. 2018. Prediction of alcohol abused individuals using artificial neural network. Int J Inf Technol, 10: 233-237.
11. Lewenstein K, Ślubowska E, Hawłas H. 2020. Alcohol addiction diagnosis on the basis of the polysomnographic parameters. Pol J Med Phys Eng, 26: 161-167.
12. Michalski RS, Carbonell JG, Mitchell TM (Ed.). 1983. Machine learning: An artificial intelligence approach. Springer, Berlin Heidelberg, pp: 582.
13. Poulton MM. 2001. Multi-layer perceptrons and back-propagation learning. In: Handbook of Geophysical Exploration: Seismic Exploration, Pergamon, 30: 27-53.
14. Shahriar A, Faisal F, Mahmud SU, Chakrabarti A, Alam MGR. 2019. A machine learning approach to predict vulnerability to drug addiction. In: 2019 22nd Int Conf Comput Inf Technol (ICCIT), Dhaka, Bangladesh, pp: 1-7.
15. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A, Kaiser Ł, Polosukhin I. 2017. Attention is all you need. Adv Neural Inf Process Syst 31st Conf Neural Inf Process Syst, Long Beach, CA, USA, pp:45-46.