Identifying a Drug Addict Person Using Artificial Neural Networks
Use and abuse of drugs by teens is very common and can have dangerous consequences. The drugs contribute to physical and sexual aggression such as assault or rape. Some teenagers regularly use drugs to compensate for depression, anxiety or a lack of positive social skills. Teen resort to smoking should not be minimized because it can be "gateway drugs" for other drugs (marijuana, cocaine, hallucinogens, inhalants, and heroin). The combination of teenagers' curiosity, risk taking behavior, and social pressure make it very difficult to say no. This leads most teenagers to the questions: "Will it hurt to try once?" Nowadays, technological advances are changing our lives very rapidly and adding a lot of technologies that help us to track the risk of drug abuse such as smart phones, Wireless Sensor Networks (WSNs), Internet of Things (IoT), etc. This technique may help us to early discovery of drug abuse in order to prevent an aggravation of the influence of drugs on the abuser. In this paper, we have developed a Decision Support System (DSS) for detecting the drug abuse using Artificial Neural Network (ANN); we used a Multilayer Perceptron (MLP) feed-forward neural network in developing the system. The input layer includes 50 variables while the output layer contains one neuron which indicates whether the person is a drug addict. An iterative process is used to determine the number of hidden layers and the number of neurons in each one. We used multiple experiment models that have been completed with Log-Sigmoid transfer function. Particularly, 10-fold cross validation schemes are used to access the generalization of the proposed system. The experiment results have obtained 98.42% classification accuracy for correct diagnosis in our system. The data had been taken from 184 cases in Jordan according to a set of questions compiled from Specialists, and data have been obtained through the families of drug abusers.
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 NIDA, Drug, Brains and Behavior: The Science of Addiction. National Institutes of Health Publication No. 07-5605, National Institute on Drug Abuse, 2007.
 Renthal, William, and Eric J. Nestler. "Epigenetic mechanisms in drug addiction." Trends in molecular medicine 14.8 (2008): 341-350.
 Schwan, Sofie, et al. "A signal for an abuse liability for pregabalin—results from the Swedish spontaneous adverse drug reaction reporting system." European journal of clinical pharmacology 66.9 (2010): 947-953.
 Pudenz, Kristen L., and Daniel A. Lidar. "Quantum adiabatic machine learning." Quantum information processing 12.5 (2013): 2027-2070.
 Bottou, Léon. "From machine learning to machine reasoning." Machine learning 94.2 (2014): 133-149.
 Fausett, Laurene. Fundamentals of neural networks: architectures, algorithms, and applications. Prentice-Hall, Inc., 1994.
 Michalski, Ryszard S., Jaime G. Carbonell, and Tom M. Mitchell, eds. Machine learning: An artificial intelligence approach. Springer Science & Business Media, 2013.
 Haykin, Simon, and Richard Lippmann. "Neural Networks, A Comprehensive Foundation." International Journal of Neural Systems 5.4 (1994): 363-364.
 Seung, Sebastian. "Multilayer perceptrons and backpropagation learning. 9.641 Lecture4. 1-6." (2002).
 Araghinejad, Shahab. Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering. Vol. 67. Springer Science & Business Media, 2013.
 Isa, Iza Sazanita, et al. "Comparisons of MLP transfer functions for different classification classes." Control System, Computing and Engineering (ICCSCE), 2012 IEEE International Conference on. IEEE, 2012.
 Heskes, Tom. "Practical confidence and prediction." Advances in Neural Information Processing Systems 9: Proceedings of the 1996 Conference. Vol. 9. MIT Press, 1997.
 J. Frazier, 2013, Health IT helping to fight the prescription drug abuse epidemic, ONC behavioral health subject matter expert, http://www.healthit.gov/buzz-blog/health-innovation/health-helping-fight-prescription-drug-abuse-epidemic/ (accessed September 19, 2015).
 Goldstein, Rita Z., et al. "The neurocircuitry of impaired insight in drug addiction." Trends in cognitive sciences 13.9 (2009): 372-380.
 Fletcher, Richard Ribón, et al. "Wearable sensor platform and mobile application for use in cognitive behavioral therapy for drug addiction and PTSD." Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE. IEEE, 2011.
 Bickel, Warren K., Darren R. Christensen, and Lisa A. Marsch. "A review of computer-based interventions used in the assessment, treatment, and research of drug addiction." Substance use & misuse 46.1 (2011): 4-9.
 Koh, Hian Chye, and Gerald Tan. "Data mining applications in healthcare." Journal of healthcare information management 19.2 (2011): 65.
 Viveros, Marisa S., John P. Nearhos, and Michael J. Rothman. "Applying data mining techniques to a health insurance information system." VLDB. 1996.
 Hall, M., et al. "The WEKA data mining software: an update. ACM SIGKDD Explor Newslett 2009; 11: 10–8."
 Bouckaert, Remco R., et al. "WEKA---Experiences with a Java Open-Source Project." The Journal of Machine Learning Research 11 (2010): 2533-2541.