An urgent precaution system to detect students at risk of substance abuse through classification algorithms

In recent years, the use of addictive drugs and substances has turned out to be a challenging social problem worldwide. The illicit use of these types of drugs and substances appears to be increasing among elementary and high school students. After becoming addicted to drugs, life becomes unbearable and gets even worse for their users. Scientific studies show that it becomes extremely difficult for an individual to break this habit after being a user. Hence, preventing teenagers from addiction becomes an important issue. This study focuses on an urgent precaution system that helps families and educators prevent teenagers from developing this type of addiction. The aim of this study is to detect a teenager's probability of being a drug abuser using classification algorithms in machine learning and data mining. The objective is not to test the classifiers theoretically on the benchmark datasets, but rather to use this study as a basis for advanced and detailed studies in this field in the future. This paper not only uses a special dataset but also focuses on psychometrics and statistics. The findings of this study show that if there is a computed high risk for a teenager, some precautions, if necessary, may be taken by educators and parents to keep the teenager away from those substances.

Anahtar Kelimeler:

Substance abuse, risk assessment, data mining, machine learning, classification algorithms

An urgent precaution system to detect students at risk of substance abuse through classification algorithms

Keywords:

Substance abuse, risk assessment, data mining, machine learning, classification algorithms,

PDF

___

K. ¨ Ogel, Characteristics of Adolescent Volatile-substance Abusers - UMATEM Data, ˙Istanbul, Bakırk¨ oy Ruh ve Sinir Hastalıkları Hastanesi, 2004.
S. Kırcan, The Relationship Between Peer Pressure, Internal versus External Locus of Control and Adolescent Substance Use, MSc, Bo˘ gazi¸ ci University, ˙Istanbul, 2006.
D.C. Kimmel, I.B. Weiner, Adolescence: A Developmental Transition, 2nd ed., New York, Wiley, 1995.
M. Windle, R.C. Windle, Alcohol and other substance use and abuse, In: G.R. Adams, M.D. Berzonsky, M. Windle, R.C. Windle, editors, Blackwell Handbook of Adolescence, Malden, MA, USA, Blackwell Publishing, 2003.
O.G. Bukstein, Adolescent Substance Abuse: Assessment, Prevention and Treatment, New York, Wiley, 1995.
F. Bulut, Detecting Students at Risk of Substance Abuse by Using Data Mining Classification Algorithms, MSc, Fatih University Graduate School of Technical Sciences, ˙Istanbul, 2010.
B. G¨ ulkan, Personality and Socio-demographic Traits of Heroin Addicts, MSc, ˙Istanbul University, ˙Istanbul, 1994. I. Seyman, Dimensions of the Narcotics Issue in Turkey, MSc, Ankara University, Ankara, 2000.
C. Zor, Views of Student Families for Secondary Education about the Risks of Drug Use and the Ways of Protection, MSc, Ankara University, Ankara, 2005.
G. Erdem, C.Y. Eke, K. ¨ Ogel, S. Tanver, “Peer characteristics and substance use among high school students”, Journal of Dependence, Vol. 7, pp. 111–116, 2006.
C. Aydın, “A socio-demographic evaluation of cases applying to a child and adolescent dependency centre during a period of two years attending”, Journal of Dependence, Vol. 7, pp. 31–37, 2006.
M.S. Can, Substance Dependence Habits Observed in the Second Grades of Primary Students, MSc, Sakarya University, Sakarya, Turkey, 2007.
L. Tuncer, An Assay on the Role and Importance of Domestic Safety and National Ethics Factors in Struggle against Substance Addiction from Republic Era to Date, MSc, Fırat University, Elazı˘ g, Turkey, 2007.
M.H. Dunham, Data Mining: Introductory and Advanced Topics, 2nd ed., Upper Saddle River, NJ, USA, Prentice Hall, 2005.
L. Jiang, H. Zhang, Z. Cai, Dynamic K-Nearest-Neighbor Naive Bayes with Attribute Weighted, Berlin, Springer, 200 K. Chen, L. Liu, A survey of multiplicative perturbation for privacy-preserving data mining, in: C.C. Aggarwal, P.S. Yu, editors, Privacy-Preserving Data Mining: Models and Algorithms, New York, Springer, pp. 157–181, 2008. F. Nigsch, A. Bender, B. Buuren, J. Tissen, E. Nigsch, J.B.O. Mitchell, “Melting point prediction employing knearest neighbor algorithms and genetic parameter optimization”, Journal of Chemical Information and Modeling, Vol. 46, pp. 2412–2422, 2006.
P. Hall, B.U. Park, R.J. Samworth, “Choice of neighbor order in nearest-neighbor classification”, Annals of Statistics, Vol. 36, pp. 2135–2152, 2008.
T.M. Mitchell, Machine Learning, New York, McGraw-Hill, 1997.
M. Moradian, A. Barani, “KNNBA: k-nearest-neighbor-based association algorithm”, Journal of Theoretical and Applied Information Technology, Vol. 6, pp. 123–130, 2009.
M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, P.I. Witten, “The WEKA data mining software: an update”, ACM SIGKDD Explorations Newsletter, Vol. 11, pp. 10–18, 2009.
T. Hill, P. Lewicki, Na¨ıve Bayes Classifier Introductory Overview, STATISTICS Methods and Applications, StatSoft, Tulsa, OK, USA, 2007.
R. Kohavi, “Scaling up the accuracy of na¨ıve-Bayes classifiers: a decision-tree hybrid”, Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Vol. 7, pp. 202–207, 1996.
W. Peng, J. Chen, H. Zhou, An Implementation of ID3 - Decision Tree Learning Algorithm, Sydney, Australia, University of New South Wales, 2010.
J.R. Quinlan, “Induction of decision trees”, Machine Learning, Vol. 1, pp. 81–106, 1986.
J.R. Quinlan, C4.5: Programs for Machine Learning, San Mateo, CA, USA, Morgan Kaufmann Publishing, 1993. D.M. Farid, H. Nouria, M.Z. Rahman, “Combining na¨ıve Bayes and decision tree for adaptive intrusion detection”, International Journal of Network Security & Its Applications, Vol. 2, pp. 12–25, 2010.
R.C. Holte, “Very simple classification rules perform well on most commonly used datasets”, Machine Learning, Vol. 11, pp. 63–91, 1993.
E. Frank, I.H. Witten, “Generating accurate rule sets without global optimisation”, Proceedings of the 15th International Conference on Machine Learning, 1988.
S. Russel, P. Norvig, Artificial Intelligence: A Modern Approach, 2nd ed., Upper Saddle River, NJ, USA, Prentice Hall, 2003.