Classification of Alcohol Consumption among Secondary School Students

Shamala Palaniappan, Norhamreeza A Hameed, Aida Mustapha, Noor Azah Samsudin

Abstract


In 2016, the National Institute of Health reported that 26% of 8th graders, 47% of 10th graders, and 64% of 12th graders have all had experience in consuming alcoholic drinks. This finding indicates an accelerating trend in alcohol use among school students, hence a growing concerns among the public. To address this issue, this paper is set to model the alcohol consumption data among the secondary school students and attempt to predict the alcohol consumption behaviors among them. A set of classification experiments are carried out and the classification accuracies are compared between two variations of neural network algorithms; a self-tuning multilayer perceptron classifier (AutoMLP) against the standard MLP using the student alcohol consumption dataset. It is found that AutoMLP produced better accuracy of 64.54% than neural network with 61.78%.


Keywords


neural network; classification; data mining; alcohol consumption

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References


Aggarwal, C.C. (2015). Data mining. Springer.

Cortez, P. & Silva, A.M.G. (2008). Using data mining to predict secondary school student performance.

Crutzen, R., Giabbanelli, P.J., Jander, A., Mercken, L., & Vries, H.d. (2015). Identifying binge drinkers based on parenting dimensions and alcohol-specific parenting practices: building classifiers on adolescent-parent paired data. BMC Public Health, 15(1):747.

Gunzerath, L., Faden, V., Zakhari, S., & Warren, K. (2004). National Institute on Alcohol Abuse and Alcoholism report on moderate drinking. Alcoholism: Clinical and Experimental Research, 28, 829-847.

Gupta, G. (2014). Introduction to data mining with case studies: PHI Learning Pvt. Ltd.

Hamid, N.A., Nawi, N.M., & Ghazali, R. (2011). The Effect of Adaptive Gain and Adaptive Momentum in Improving Training Time of Gradient Descent Back Propagation Algorithm on Classification Problems. Proceedings of the International Conference on Advanced Science, Engineering and Information Technology, 178-184.

Hariharan, B., Krithivasan, R., & Angel, D. (2016). Prediction of Secondary School Students’ Alcohol Addiction using Random Forest. International Journal of Computer Applications, 149(6).

Jackson, N., Denny, S., Sheridan, J., Fleming, T., Clark, T., Teevale, T., & Ameratunga, S. (2014). Predictors of drinking patterns in adolescence: A latent class analysis. Drug and Alcohol Dependence, 135: 133-139.

Lashari, S.A., Ibrahim, R., Senan, N., Yanto, I.T., & Herawan, T. (2016). Application of Wavelet De-noising Filters in Mammogram Images Classification Using Fuzzy Soft Set. Proceedings of the International Conference on Soft Computing and Data Mining, p. 529-537.

Miech, Richard A., et al. (2016). Monitoring the Future national survey results on drug use, 1975-2015: Volume I, Secondary school students.

Nawi, N.M., Hamid, N.A., Harsad, H., & Ramli, A.A. (2016). Second Order Back Propagation Neural Network (SOBPNN) Algorithm for Medical Data Classification. Proceedings of the Computational Intelligence in Information Systems: Proceedings of the Fourth INNS Symposia Series on Computational Intelligence in Information Systems (INNS-CIIS 2014). In S. Phon-Amnuaisuk and T. W. Au (Eds.), Springer International Publishing, 73-83.

Nawi, N.M., Ransing, R.S., Salleh, M.N.N., Ghazali, R., & Hamid, N.A. (2010). An Improved Back Propagation Neural Network Algorithm on Classification Problems. Database Theory and Application, Bio-Science and Bio-Technology, In Y. Zhang, A. Cuzzocrea, J. Ma, K.-i. Chung, T. Arslan, and X. Song (Eds.), 108, 177-188.

Pagnotta, F. & Amran, M.H. (2016). Using data mining to predict secondary school student alcohol consumption. Department of Computer Science, University of Camerino.

Witten, I.H., Frank, E., Hall, M.A., & Pal, C.J. (2016). Data Mining: Practical machine learning tools and techniques: Morgan Kaufmann.


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