An Empirical Comparison of Machine Learning Models for Student’s Mental Health Illness Assessment
Keywords:Student’s mental health illness, Machine learning, Feature importance
Student’s mental health problems have been explored previously in higher education literature in various contexts including empirical work involving quantitative and qualitative methods. Nevertheless, comparatively few research could be found, aiming for computational methods that learn information directly from data without relying on set parameters for a predetermined equation as an analytical method. This study aims to investigate the performance of Machine learning (ML) models used in higher education. ML models considered are Naïve Bayes, Support Vector Machine, K-Nearest Neighbor, Logistic Regression, Stochastic Gradient Descent, Decision Tree, Random Forest, XGBoost (Extreme Gradient Boosting Decision Tree), and NGBoost (Natural) algorithm. Considering the factors of mental health illness among students, we follow three phases of data processing: segmentation, feature extraction, and classification. We evaluate these ML models against classification performance metrics such as accuracy, precision, recall, F1 score, and predicted run time. The empirical analysis includes two contributions: 1. It examines the performance of various ML models on a survey-based educational dataset, inferring a significant classification performance by a tree-based XGBoost algorithm; 2. It explores the feature importance [variables] from the datasets to infer the significant importance of social support, learning environment, and childhood adversities on a student’s mental health illness.
Adams, D. R., Meyers, S. A., & Beidas, R. S., “The relationship between financial strain, perceived stress, psychological symptoms, and academic and social integration in undergraduate students”. Journal of American College Health, 64(5), 362–370, 2016. https://doi.org/10.1080/07448481.2016.1154559
Alkis, Y., Kadirhan, Z., & Sat, M., “Development and Validation of Social Anxiety Scale for Social Media Users”. Computers in Human Behavior, 72, 296–303, 2017. https://doi.org/10.1016/j.chb.2017.03.011
Auerbach, R. P., Alonso, J., Axinn, W. G., Cuijpers, P., Ebert, D. D., Green, J. G., Hwang, I., Kessler, R. C., Liu, H., Mortier, P., Nock, M. K., Pinder-Amaker, S., Sampson, N. A., Aguilar-Gaxiola, S., Al-Hamzawi, A., Andrade, L. H., Benjet, C., Caldas-de-Almeida, J. M., Demyttenaere, K., Bruffaerts, R., “Mental disorders among college students in the World Health Organization World Mental Health Surveys”. Psychological Medicine, 46(14), 2955–2970, 2016. https://doi.org/10.1017/s0033291716001665
Becker, S. P., Jarrett, M. A., Luebbe, A. M., Garner, A. A., Burns, G. L., & Kofler, M. J., “Sleep in a large, multi-university sample of college students: sleep problem prevalence, sex differences, and mental health correlates”. Sleep Health, 4(2), 174–181, 2018. https://doi.org/10.1016/j.sleh.2018.01.001
Beiter, R., Nash, R., McCrady, M., Rhoades, D., Linscomb, M., Clarahan, M., & Sammut, S., “The prevalence and correlates of depression, anxiety, and stress in a sample of college students”. Journal of Affective Disorders, 173, 90–96, 2015. https://doi.org/10.1016/j.jad.2014.10.054
Bose, I., & Mahapatra, R. K., “Business data mining — a machine learning perspective”. Information & Management, 39(3), 211–225, 2001. https://doi.org/10.1016/s0378-7206(01)00091-x
Chen, T., Xu, J., Ying, H., Chen, X., Feng, R., Fang, X., Gao, H., & Wu, J., “Prediction of Extubation Failure for Intensive Care Unit Patients Using Light Gradient Boosting Machine”. IEEE Access, 7, 150960–150968, 2019. https://doi.org/10.1109/access.2019.2946980
Falsafi, N., “A Randomized Controlled Trial of Mindfulness Versus Yoga”. Journal of the American Psychiatric Nurses Association, 22(6), 483–497, 2016. https://doi.org/10.1177/1078390316663307
Gaikwad, N. B., Tiwari, V., Keskar, A., & Shivaprakash, N. C., “Efficient FPGA Implementation of Multilayer Perceptron for Real-Time Human Activity Classification”. IEEE Access, 7, 26696–26706, 2019. https://doi.org/10.1109/access.2019.2900084
Hasanin, T., Khoshgoftaar, T. M., & Leevy, J. L., “A Comparison of Performance Metrics with Severely Imbalanced Network Security Big Data”. 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) 2019. Published. https://doi.org/10.1109/iri.2019.00026
Holland, J. H., Green, J. J., Alexander, L., & Phillips, M., “School Health Policies: Evidenced-based Programs for Policy Implementation”. Journal of Policy Practice, 15(4), 314–332, 2016. https://doi.org/10.1080/15588742.2015.1081580
Huang, J., Nigatu, Y. T., Smail-Crevier, R., Zhang, X., & Wang, J., “Interventions for common mental health problems among university and college students: A systematic review and meta-analysis of randomized controlled trials”. Journal of Psychiatric Research, 107, 1–10, 2018. https://doi.org/10.1016/j.jpsychires.2018.09.018
Hunt, J., & Eisenberg, D., “Mental Health Problems and Help-Seeking Behavior Among College Students”. Journal of Adolescent Health, 46(1), 3–10, 2010. https://doi.org/10.1016/j.jadohealth.2009.08.008
Kitzrow, M. A., “The Mental Health Needs of Today’s College Students: Challenges and Recommendations”. NASPA Journal, 41(1), 167–181, 2003. https://doi.org/10.2202/1949-6605.1310
Kitzrow, M. A., “The Mental Health Needs of Today’s College Students: Challenges and Recommendations”. NASPA Journal, 46(4), 646–660, 2009. https://doi.org/10.2202/1949-6605.5037
Matar Boumosleh, J., & Jaalouk, D., “Depression, anxiety, and smartphone addiction in university students- A cross sectional study”. PLOS ONE, 12(8), e0182239, 2017. https://doi.org/10.1371/journal.pone.0182239
Minh Dang, L., Min, K., Wang, H., Jalil Piran, M., Hee Lee, C., & Moon, H., “Sensor-based and vision-based human activity recognition: A comprehensive survey”. Pattern Recognition, 108, 107561, 2020. https://doi.org/10.1016/j.patcog.2020.107561
Miranda, R., Soffer, A., Polanco-Roman, L., Wheeler, A., & Moore, A., “Mental Health Treatment Barriers Among Racial/Ethnic Minority Versus White Young Adults 6 Months After Intake at a College Counseling Center”. Journal of American College Health, 63(5), 291–298, 2015. https://doi.org/10.1080/07448481.2015.1015024
Muzumdar, P., Basyal, G.P., and Vyas, P., "Antecedents to Student Retention: A Predictive Modelling Approach". International Journal of Contemporary Research and Review. 11(11), 21906-21913. https://doi.org/10.15520/ijcrr.v11i11.860
Pan, F., Converse, T., Ahn, D., Salvetti, F., & Donato, G., “Feature selection for ranking using boosted trees”. Proceeding of the 18th ACM Conference on Information and Knowledge Management - CIKM ’09. 2009. https://doi.org/10.1145/1645953.1646292
Pedrelli, P., Borsari, B., Lipson, S. K., Heinze, J. E., & Eisenberg, D., “Gender Differences in the Relationships Among Major Depressive Disorder, Heavy Alcohol Use, and Mental Health Treatment Engagement Among College Students”. Journal of Studies on Alcohol and Drugs, 77(4), 620–628, 2016. https://doi.org/10.15288/jsad.2016.77.620
Pedrelli, P., Nyer, M., Yeung, A., Zulauf, C., & Wilens, T., “College Students: Mental Health Problems and Treatment Considerations”. Academic Psychiatry, 39(5), 503–511, 2014. https://doi.org/10.1007/s40596-014-0205-9
Rao, H., Shi, X., Rodrigue, A. K., Feng, J., Xia, Y., Elhoseny, M., Yuan, X., & Gu, L., “Feature selection based on artificial bee colony and gradient boosting decision tree”. Applied Soft Computing, 74, 634–642, 2019. https://doi.org/10.1016/j.asoc.2018.10.036
Salminen, J., Yoganathan, V., Corporan, J., Jansen, B. J., & Jung, S. G., “Machine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content type”. Journal of Business Research, 101, 203–217, 2019. https://doi.org/10.1016/j.jbusres.2019.04.018
Schroder, H. S., Dawood, S., Yalch, M. M., Donnellan, M. B., & Moser, J. S., “The Role of Implicit Theories in Mental Health Symptoms, Emotion Regulation, and Hypothetical Treatment Choices in College Students”. Cognitive Therapy and Research, 39(2), 120–139, 2014. https://doi.org/10.1007/s10608-014-9652-6
Shah, K., Patel, H., Sanghvi, D., & Shah, M., “A Comparative Analysis of Logistic Regression, Random Forest and KNN Models for the Text Classification”. Augmented Human Research, 5(1), 2020. https://doi.org/10.1007/s41133-020-00032-0
Shenoy, D. P., Lee, C., & Trieu, S. L., “The Mental Health Status of Single-Parent Community College Students in California”. Journal of American College Health, 64(2), 152–156, 2015. https://doi.org/10.1080/07448481.2015.1057147
Silverman, J. J., Galanter, M., Jackson-Triche, M., Jacobs, D. G., Lomax, J. W., Riba, M. B., Tong, L. D., Watkins, K. E., Fochtmann, L. J., Rhoads, R. S., & Yager, J., “The American Psychiatric Association Practice Guidelines for the Psychiatric Evaluation of Adults”. American Journal of Psychiatry, 172(8), 798–802, 2015. https://doi.org/10.1176/appi.ajp.2015.1720501
Soet, J., & Sevig, T., “Mental Health Issues Facing a Diverse Sample of College Students: Results from the College Student Mental Health Survey”. NASPA Journal, 43(3), 410–431, 2006. https://doi.org/10.2202/1949-6605.1676
Stanley, I. H., Hom, M. A., & Joiner, T. E., “Modifying mental health help-seeking stigma among undergraduates with untreated psychiatric disorders: A pilot randomized trial of a novel cognitive bias modification intervention”. Behaviour Research and Therapy, 103, 33–42, 2018. https://doi.org/10.1016/j.brat.2018.01.008
Vyas, P., Reisslein, M., Rimal, B. P., Vyas, G., Basyal, G. P., and Muzumdar, P., "Automated Classification of Societal Sentiments on Twitter with Machine Learning". IEEE Transactions on Technology and Society, 2021. doi: 10.1109/TTS.2021.3108963
Vyas, P., Liu, J., and El-Gayar, O., "Fake News Detection on the Web: An LSTM-based Approach". AMCIS 2021 Proceedings, 5, 2021. https://aisel.aisnet.org/amcis2021/virtual_communities/virtual_communities/5
Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., Zhou, X., Ben-Zeev, D., & Campbell, A. T., “StudentLife”. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 2014. https://doi.org/10.1145/2632048.2632054
Wang, R., Wang, W., daSilva, A., Huckins, J. F., Kelley, W. M., Heatherton, T. F., & Campbell, A. T., “Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing”. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(1), 1–26, 2018. https://doi.org/10.1145/3191775
Woodford, M. R., Han, Y., Craig, S., Lim, C., & Matney, M. M., “Discrimination and Mental Health Among Sexual Minority College Students: The Type and Form of Discrimination Does Matter”. Journal of Gay & Lesbian Mental Health, 18(2), 142–163, 2014. https://doi.org/10.1080/19359705.2013.833882
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