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References
AIHW. Mental health: prevalence and impact. In: Mental Health Services in Australia. AIHW, Canberra (2022)
Ay, B., et al.: Automated depression detection using deep representation and sequence learning with EEG signals. J. Med. Syst. 43, 1–12 (2019)
Burdisso, S.G., Errecalde, M., Montes-y-Gómez, M.: A text classification framework for simple and effective early depression detection over social media streams. Expert Syst. Appl. 133, 182–197 (2019)
Zhang, B., et al.: Ubiquitous depression detection of sleep physiological data by using combination learning and functional networks. IEEE Access 8, 94220–94235 (2020)
Habtamu, K., et al.: Interventions to improve the detection of depression in primary healthcare: systematic review. Syst. Rev. 12(1), 1–28 (2023)
Nisar, A., et al.: Prevalence of perinatal depression and its determinants in Mainland China: a systematic review and meta-analysis. J. Affect. Disord. 277, 1022–1037 (2020)
Zenebe, Y., Akele, B., Necho, M.: Prevalence and determinants of depression among old age: a systematic review and meta-analysis. Ann. Gen. Psychiatry 20(1), 1–19 (2021)
Othman, N., et al.: Perceived impact of contextual determinants on depression, anxiety and stress: a survey with university students. Int. J. Ment. Heal. Syst. 13(1), 1–9 (2019)
Islam, M.R., et al.: Depression detection from social network data using machine learning techniques. Health Inf. Sci. Syst. 6(1), 1–12 (2018)
Nguyen, M.-H., et al.: A dataset of students’ mental health and help-seeking behaviors in a multicultural environment. Data 4(3), 124 (2019)
Priya, A., Garg, S., Tigga, N.P.: Predicting anxiety, depression and stress in modern life using machine learning algorithms. Procedia Comput. Sci. 167, 1258–1267 (2020)
Cacheda, F., et al.: Early detection of depression: social network analysis and random forest techniques. J. Med. Internet Res. 21(6), e12554 (2019)
Pflueger, M.O., et al.: Predicting general criminal recidivism in mentally disordered offenders using a random forest approach. BMC Psychiatry 15(1), 1–10 (2015)
Haque, U.M., Kabir, E., Khanam, R.: Detection of child depression using machine learning methods. PLoS ONE 16(12), e0261131 (2021)
Green, H., et al.: Mental Health of Children and Young People in Great Britain. Palgrave Macmillan, Basingstoke (2005)
Kubinger, K.D.: On artificial results due to using factor analysis for dichotomous variables. Psychol. Sci. 45(1), 106–110 (2003)
Kursa, M.B., Jankowski, A., Rudnicki, W.R.: Boruta–a system for feature selection. Fund. Inform. 101(4), 271–285 (2010)
Kursa, M.B.: Boruta for those in a hurry (2020)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Cortes, C., Vapnik, V.: Support vector machine. Mach. Learn. 20(3), 273–297 (1995)
Hosmer Jr., D.W., Lemeshow, S., Sturdivant, R.X.: Applied Logistic Regression, vol. 398. Wiley, Hoboken (2013)
Soloff, C., Lawrence, D., Johnstone, R.: Sample Design. Australian Institute of Family Studies, Melbourne (2005)
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Haque, U.M., Kabir, E., Khanam, R. (2023). Detection of Depression and Its Likelihood in Children and Adolescents: Evidence from a 15-Years Study. In: Li, Y., Huang, Z., Sharma, M., Chen, L., Zhou, R. (eds) Health Information Science. HIS 2023. Lecture Notes in Computer Science, vol 14305. Springer, Singapore. https://doi.org/10.1007/978-981-99-7108-4_1
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