Abstract
Depression is a dreadful mental disorder affecting negatively one’s way of thinking and ability of functioning while the person may not be aware of it. The prevalence of depression is high among the young generation of developing countries because of ever-increasing academic and career-related distress, job uncertainty, and family problems. In Bangladesh, there is a dearth of information about the predictors of depression among university students, so is a model to identify them. The information is important for the prevention of depression and the promotion of mental health. The data set we used for this research was built on the data collected by a questionnaire circulated through social media. Using Pearson’s chi-squared test and back elimination method, we have identified the key feature variables. We used six different ML classifiers to build the classification model that is capable of detecting the presence of depression. Among the six, the stacking classifier with 24 attributes shows the highest accuracy.
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References
Sadock, B.J., Sadock, V.A., Ruiz, P., Kaplan, H.I.: Kaplan and Sadocks Comprehensive Textbook of Psychiatry. Wolters Kluwer (2017)
Ibrahim, A.K., Kelly, S.J., Adams, C.E., Glazebrook, C.: A systematic review of studies of depression prevalence in university students. J. Psychiatr. Res. 47(3), 391–400 (2013)
Islam, S., Akter, R., Sikder, T., Griffiths, M.D.: Prevalence and factors associated with depression and anxiety among first-year university students in Bangladesh: a cross-sectional study. Int. J. Ment. Health Addict. 1–14 (2020)
Miah, Y., Prima, C.N.E., Seema, S.J., Mahmud, M., Shamim Kaiser, M.: Performance comparison of machine learning techniques in identifying dementia from open access clinical datasets. In: Saeed, F., Al-Hadhrami, T., Mohammed, F., Mohammed, E. (eds.) Advances on Smart and Soft Computing. Advances in Intelligent Systems and Computing, pp. 79–89. Springer, Singapore (2021)
Choudhury, A.A., Khan, M.R.H., Nahim, N.Z., Tulon, S.R., Islam, S., Chakrabarty, A.: Predicting depression in Bangladeshi undergraduates using machine learning. In: 2019 IEEE Region 10 Symposium (TENSYMP), pp. 789–794. IEEE (2019)
Sarokhani, D., Delpisheh, A., Veisani, Y., Sarokhani, M.T., Manesh, R.E., Sayehmiri, K.: Prevalence of depression among university students: a systematic review and meta-analysis study. In: Depression Research and Treatment, vol. 2013 (2013)
Mahmud M., Kaiser M.S.: Machine learning in fighting pandemics: a COVID-19 case study. In: Santosh, K., Joshi A. (eds.) COVID-19: Prediction, Decision-Making, and its Impacts. Lecture Notes on Data Engineering and Communications Technologies, pp. 77–81. Springer, Singapore (2021)
Gollust, S.E., Eisenberg, D., Golberstein, E.: Prevalence and correlates of self-injury among university students. J. Am. Coll. Health 56(5), 491–498 (2008)
Tasnim, R., Islam, M.S., Sujan, M.S.H., Sikder, M.T., Potenza, M.N.: Suicidal ideation among Bangladeshi university students early during the covid-19 pandemic: prevalence estimates and correlates. Child. Youth Serv. Rev. 119, 105703 (2020)
Bachmann, S.: Epidemiology of suicide and the psychiatric perspective. Int. J. Environ. Res. Public Health 15(7), 1425 (2018)
Bostanci, M., Ozdel, O., Oguzhanoglu, N.K., Ozdel, L., Ergin, A., Ergin, N., Atesci, F., Karadag, F.: Depressive symptomatology among university students in Denizli, Turkey: prevalence and sociodemographic correlates. Croat. Med. J. 46(1), 96–100 (2005)
Bayram, N., Bilgel, N.: The prevalence and socio-demographic correlations of depression, anxiety and stress among a group of university students. Soc. Psychiatry Psychiatr. Epidemiol. 43(8), 667–672 (2008)
Lim, A.Y., Lee, S.-H., Jeon, Y., Yoo, R., Jung, H.-Y.: Job-seeking stress, mental health problems, and the role of perceived social support in university graduates in Korea. J. Korean Med. Sci. 33(19) (2018)
Saunders, D.E., Peterson, G.W., Sampson, J.P., Jr., Reardon, R.C.: Relation of depression and dysfunctional career thinking to career indecision. J. Vocat. Behav. 56(2), 288–298 (2000)
Mamun, M.A., Hossain, M.S., Griffiths, M.D.: Mental health problems and associated predictors among Bangladeshi students. Int. J. Ment. Health Addict. 1–15 (2019)
Mahmud, M., Kaiser, M.S., McGinnity, T.M., Hussain, A.: Deep learning in mining biological data. Cogn. Comput. 13(1), 1–33 (2021)
Kaiser, M.S., Mahmud, M., Noor, M.B.T., Zenia, N.Z., Al Mamun, S., Mahmud, K.A., Azad, S., Aradhya, V.M., Stephan, P., Stephan, T. et al.: iWorksafe: towards healthy workplaces during covid-19 with an intelligent phealth app for industrial settings. IEEE Access 9, 13814–13828 (2021)
Daimi, K., Banitaan, S.: Using data mining to predict possible future depression cases. Int. J. Public Health Sci. (IJPHS) 3(4), 231–240 (2014)
Mahmoud, J., et al.: The relationship among young adult college students’ depression, anxiety, stress, demographics, life satisfaction, and coping styles. Issues Ment. Health Nurs. 33(3), 149–156 (2012)
Bhakta, I., Sau, A.: Prediction of depression among senior citizens using machine learning classifiers. Int. J. Comput. Appl. 144(7), 11–16 (2016)
Adewuya, A.O., Ola, B.A., Aloba, O.O., Mapayi, B.M., Oginni, O.O.: Depression amongst Nigerian university students. Soc. Psychiatry Psychiatr. Epidemiol. 41(8), 674–678 (2006)
Martin, A., Rief, W., Klaiberg, A., Braehler, E.: Validity of the brief patient health questionnaire mood scale (PHG-9) in the general population. Gen. Hosp. Psychiatry 28(1), 71–77 (2006)
Kroenke, K., Spitzer, R.L.: The PHG-9: a new depression diagnostic and severity measure. Psychiatr. Ann. 32(9), 509–515 (2002)
Bolboacă, S.D., Jäntschi, L., Sestraş, A.F., Sestraş, R.E., Pamfil, D.C.: Pearson-fisher chi-square statistic revisited. Information 2(3), 528–545 (2011)
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Munir, U.B., Kaiser, M.S., Islam, U.I., Siddiqui, F.H. (2022). Machine Learning Classification Algorithms for Predicting Depression Among University Students in Bangladesh. In: Kaiser, M.S., Ray, K., Bandyopadhyay, A., Jacob, K., Long, K.S. (eds) Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering. Lecture Notes in Networks and Systems, vol 348. Springer, Singapore. https://doi.org/10.1007/978-981-16-7597-3_6
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DOI: https://doi.org/10.1007/978-981-16-7597-3_6
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