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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

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References


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