Analysis of Significant Influence towards Students’ Depression using Neural Network and Classification Tree Techniques

Norhatta Mohd, Yasmin Yahya

Abstract


Students’ depression is an important issue to most of the higher learning institutions. Although this issue has been investigated by many researchers using statistical analysis and data mining techniques, this paper focused on the performance of Classification Tree and Artificial Neural Network techniques of depression among Engineering Technology students at Universiti Kuala Lumpur (UniKL) Malaysian Institute of Information Technology (MIIT). Various factors that may likely influence the students’ depression were identified. Stress factors, social factors (interpersonal and intrapersonal), environment factor as well as demographic factors attribute to predict the students’ depression. The performances of these techniques are compared, based on accuracy. From the findings of the analysis, social intra-personal stress was found significantly contribute to students’ depression. Performances of both methods were compared using cross validation analysis. Artificial Neural Network has the least of error rate and has the highest accuracy; therefore, Artificial Neural Network is the best technique to classify in this data set.


Keywords


Depression, Stress Factor, Neural Network, Classification Tree, Model Performance

Full Text:

PDF

References


Stark, K.D., Brookman, C.S. 1994. Theory and f amily-school intervention. In: Fine, J.M., Carlson, C. (Eds.), The Handbook of Family-school Intervention: A System Perspective. Massachusetts, Allyn and Bacon

Anson, O., Bernstein, J., Hobfoll, S.E. 1984. Anxiety and performance in two ego threatening situation. Journal of Personality Assessment 48, 168–172

Dusselier, L., Dunn, B., Wang, Y., Shelley II, M.C., Whalen, D.F. 2005. Personal health, academic, and environmental predictors of stress for residence hall students. Journal of American College Health 54, 15–24

Stewart-Brown, S., Evans, J., Patterson, J., Petersen, S., Doll, H., Balding, J., Regis, D. 2000. The health of students in institutes of higher education: an important public health problem? Journal of Public Health Medicine 22, 492–499.

Ali, B.S., Rahbar, M.H., Naeem, S., Tareen, A.L., Gui, A., Samad, L. 2002. Prevalence of and factors associated with anxiety and depression among women in a lower middle class semi-urban community of Karachi, Pakistan. Journal of the Pakistan Medical Association 52, 513–517

Eisenberg, D., Golberstein, E., Gollust, S., Hefner, J. 2007. Prevalence and correlates of depression, anxiety and suicidality among university students. American Journal of Orthopsychiatry 77, 534–542

Marwan Zaid Bataineh. 2013. Academic Stress Among Undergraduate Students: The Case Of Education Faculty At King Saud University. International Interdisciplinary Journal , Vol 2, Issue1, Jan 2013.

Norhatta, M., Yasmin,Y., Naziren, N., Siti Nabilah, A.S. 2016. Assessing Stress towards Depression among Universiti Kuala Lumpur Malaysian Institute of Information Technology (UniKL MIIT) Students. Advanced Science Letters. August 2016. Vol 22, No 8

Yonghee L ., Sangmun S. Job stress evaluation using response surface data mining. International Journal of Industrial Ergonomics. April 2014. 40 (2010) 379-385

Varun K., Anupama C. An Empirical Study of the Applications of Data Mining Techniques in Higher Education. International Journal of Advanced Computer Science and Applications,Vol. 2, No.3, March 2011

Sellappan P., Rafiah A. Intelligent Heart Disease Prediction System Using Data Mining Techniques. International Journal of Computer Science and Network Security, VOL.8 No.8, August 2008

Han, J., Kamber, M.: Data Mining Concepts and Techniques,.Morgan Kaufmann Publishers, 2006

Charly, K.: “Data Mining for the Enterprise”, 31st Annual Hawaii Int. Conf. on System Sciences, IEEE Computer, 7,295-304, 1998.

Amirah M.S., Wahidah H., Nur’aini A.R. A Review on Predicting Student’s Performance using Data Mining Techniques. Procedia Computer Science 72 ( 2015 ) 414 – 422

M. M. Quadri, N. Kalyankar, Drop out feature of student data for academic performance using decision tree techniques, Global Journal of Computer Science and Technology 10 (2).

E. Osmanbegovi´c, M. Sulji´c, Data mining approach for predicting student performance, Economic Review 10 (1).

S. Natek, M. Zwilling, Student data mining solution– knowledge management system related to higher education institutions, Expert systemswith applications 41 (14) (2014) 6400–6407.

P. M. Arsad, N. Buniyamin, J.-l. A. Manan, A neural network students’ performance prediction model (nnsppm), in: Smart Instrumentation, Measurement and Applications (ICSIMA), 2013 IEEE International Conference on, IEEE, 2013, pp. 1–5.

G. Gray, C. McGuinness, P. Owende, An application of classification models to predict learner progression in tertiary education, in: Advance Computing Conference (IACC), 2014 IEEE International, IEEE, 2014, pp. 549–554.

Tuckman. 1978. B.W. Conducting educational research. New York: Harcont Brace Jovanovich Inc.

Badriyah T, Briggs J S and Prytherch D R. 2012. Decision Trees for Predicting Risk of Mortality using Routinely Collected Data. World Academy of Science, Engineering and Technology 62 2012.

Badriyah T, Briggs J S and Prytherch D R. 2012. Decision Trees for Predicting Risk of Mortality using Routinely Collected Data. World Academy of Science, Engineering and Technology 62 2012

Altmaier, E. M. 1983. Helping students manage stress. San Francisco: Jossey-Boss Inc.

Nelson, N. G., Dell’Oliver, C., Koch, C., & Buckler, R. 2001. Stress, coping, and success among graduate students in clinical psychology. Psychological Reports, 88, 759-767.

Roberts, G. H., & White, W. G. 1989. Health and stress in developmental college students. Journal of College Student Development, 30, 515-521.

Denise Pfeiffer. 2001. Academic and environmental stress among undergraduate and graduate college students: a literature review. The Graduate School University of Wisconsin-StoutMenomonie, WI 54751 .

Fisher, S. 1994. Stress in academic life. New York: Open University Press.

N. Kumarswamy and P.O. Ebigbo, Stress among second year medical students – A comparative study, Indian J Clin Psychol., 16(1989), 21-23.

Cohen, S., Janicki-Deverts, D., & Miller, G. E. (2007). Psychological stress and disease. Journal of the American Medical Association, 298, 1685–1687.

V.O. Oladokun, A.T. Adebanjo and O.E. Charles-Owaba. Predicting Students’ Academic Performance using Artificial Neural Network:A Case Study of an Engineering Course. The Pacific Journal of Science and Technology, Volume 9. Number 1. May-June 2008 (Spring

Amirah M. S, Wahidah H and Nur’aini A.R. A Review on Predicting Student’s Performance using Data Mining Techniques. The Third Information Systems International Conference, Procedia Computer Science 72 ( 2015 ) 414 – 422.

Sellappan P. and Rafiah A.. Intelligent Heart Disease Prediction System Using Data Mining Techniques. IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.8, August 2008

M. Mayilvaganan, D. Kalpanadevi, Comparison of classification techniques for predicting the performance of student,s academic environment,in: Communication and Network Technologies (ICCNT), 2014 International Conference on, IEEE, 2014, pp. 113–118.

K. Bunkar, U. K. Singh, B. Pandya, R. Bunkar, Data mining: Prediction for performance improvement of graduate students using classification,in: Wireless and Optical Communications Networks (WOCN), 2012 Ninth International Conference on, IEEE, 2012, pp. 1–5.

P. M. Arsad, N. Buniyamin, J.-l. A. Manan, A neural network students’ performance prediction model (nnsppm), in: Smart Instrumentation, Measurement and Applications (ICSIMA), 2013 IEEE International Conference on, IEEE, 2013, pp. 1–5.

T. Mishra, D. Kumar, S. Gupta, Mining students’ data for prediction performance, in: Proceedings of the 2014 Fourth International Conference on Advanced Computing & Communication Technologies, ACCT ’14, IEEE Computer Society, Washington, DC, USA, 2014, pp. 255–262.doi:10.1109/ACCT.2014.105.URL http://dx.doi.org/10.1109/ACCT.2014.105


Refbacks

  • There are currently no refbacks.




Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Revista Publicando.

Licencia de Creative Commons

 

This Content is available under licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional.