Shabnam Shadroo, Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Rahil Hosseini, Department of Computer Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran
Mental health is an important issue in students' study in a university. The objective is to apply and compare different classification methods. Furthermore, it utilizes the application of combined classification methods to assist psychologists’ decisions. Ten different classifiers were applied for classifying the students in to two groups of those in need of consultation and those not. Two methods have been suggested. First, the output of four classifiers with a lower accuracy was combined using maximum probability voting. The accuracy was 94.07 % and the area under ROC curve was 98.4%. Second, classifiers with a lower False Negative rate and False Positive rate were combined using two voting methods; majority voting and maximum probability voting. Accuracy of these methods were 95.97% and 96.66% and the area under ROC curves were 95.5%. and 99.4% respectively. The results are promising for assisting the process of mental health assessment of students.
Bayes Net, Logistic, RBF, SVM, Random Forest, Naïve Bayes Tree, Classification Via Regression, Mental Health, Voting, Classification
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APA:
Shadroo, S., & Hosseini, R. (2021). Data Mining Models Based on Voting Method Applied to Assessment of Mental Health in University Students. Journal of Community Healthcare and Development (JCHD), ISSN: 2652-6026, NADIA, 2(1), 31-44. doi: 10.33832/jchd.2021.2.1.04.
MLA:
Shadroo, Shabnam, et al. “Data Mining Models Based on Voting Method Applied to Assessment of Mental Health in University Students.” Journal of Community Healthcare and Development, ISSN: 2652-6025, NADIA, vol. 2, no. 1, 2021, pp. 31-44. JCHD, http://article.nadiapub.com/JCHD/vol2_no1/4.html.
IEEE:
[1] S. Shadroo, and R. Hosseini, "Data Mining Models Based on Voting Method Applied to Assessment of Mental Health in University Students." Journal of Community Healthcare and Development (JCHD), ISSN: 2652-6025, NADIA, vol. 2, no. 1, pp. 31-44, April 2021.
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