Mineração de Dados aplicada à Saúde Mental de Estudantes Universitários: Uma Revisão Sistemática
Abstract
This work presents a systematic review intending to evaluate the current state of the art regarding the application of data mining techniques for investigation of mental disorders in university students. Taken into account the use of search strings, 187 papers were found. Besides, as a result of the abstracts reading as well as the application of the inclusion and exclusion criteria, 53 works were selected for further complete analysis. Finally, 25 papers have been accepted for data extraction. Accordingly, herein it was possible to identify the most commonly used algorithm and techniques, mental disorders and related variables, key devices and sample nationalities. Among the most studied disorders, anxiety and depression stand out. These were studied, mainly, by the SVM, neuro fuzzy and KNN techniques. In relation to Brazilian university students, it is possible to highlight the scarcity of studies in relation to mental health, using data mining techniques.
References
Ahujaa, R. and Bangab, A. (2019). Mental stress detection in university students using machine learning algorithms. Procedia Computer Science, 152:349–353.
Alonso, S. G., de La Torre-Díez, I., Hamrioui, S., Lopez-Coronado, M., Barreno, D. C., Nozaleda, L. M., and Franco, M. (2018). Data mining algorithms and techniques in mental health: a systematic review. Journal of medical systems, 42(9):161.
Association, A. P. et al. (2014). DSM-5: Manual diagnostico e estat ístico de transtornos mentais. Artmed Editora.
Beck, A., Steer, R., and Brown, G. (1996). Manual for the beck depression inventory second edition (bdi-ii) psychological corporation. San Antonio, TX.
Bian, J., Barnes, L. E., Chen, G., and Xiong, H. (2017). Early detection of diseases using electronic health records data and covariance-regularized linear discriminant analysis. In 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pages 457–460. IEEE.
BRASIL, O. (2016). Depressao: o que voc e precisa saber.
Cohen, S., Kamarck, T., and Mermelstein, R. (1983). A global measure of perceived stress. Journal of health and social behavior, pages 385–396.
Cui Yuan (2014). Data mining techniques with its application to the dataset of mental health of college students. In 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA), pages 391–393.
da Victoria, M. S., Bravo, A., Felix, A. K., Neves, B. G., Rodrigues, C. B., Ribeiro, C. C. P., Canejo, D., Coelho, D., Sampaio, D., Esteves, I. M., et al. (2015). Níveis de ansiedade e depressao em graduandos da universidade do estado do rio de janeiro (uerj). Encontro: Revista de Psicologia, 16(25):163–175.
Day, V., McGrath, P. J., and Wojtowicz, M. (2013). Internet-based guided self-help for university students with anxiety, depression and stress: a randomized controlled clinical trial. Behaviour research and therapy, 51(7):344–351.
de Rezende, C., Abrao, C., Coelho, E., and da Silva Passos, L. (2008). Prevalencia de sintomas depressivos entre estudantes de medicina da universidade federal de uberlandia. Rev Bras Educ Med, 32(3):315–23.
Derogatis, L. (1992). Administration, scoring and procedures manual-ii (baltimore, md: Clinical psychometric research)(scl-90-r).
Devi, S., Kumar, S., and Kushwaha, G. S. (2016). An adaptive neuro fuzzy inference system for prediction of anxiety of students. In 2016 Eighth International Conference on Advanced Computational Intelligence (ICACI), pages 7–13. IEEE.
Egilmez, B., Poyraz, E., Zhou, W., Memik, G., Dinda, P., and Alshurafa, N. (2017). Ustress: Understanding college student subjective stress using wrist-based passive sensing. In 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pages 673–678. IEEE.
Han, J. and Kamber, M. (2005). Data mining : concepts and techniques. Kaufmann, San Francisco [u.a.].
Hou, Y., Xu, J., Huang, Y., and Ma, X. (2016). A big data application to predict depression in the university based on the reading habits. In 2016 3rd International Conference on Systems and Informatics (ICSAI), pages 1085–1089. IEEE.
Jensen, A. R. (1958). The maudsley personality inventory. Acta Psychologica, 14:314– 325.
Khosrowabadi, R., Quek, C., Ang, K. K., Tung, S. W., and Heijnen, M. (2011). A braincomputer interface for classifying eeg correlates of chronic mental stress. In The 2011 International Joint Conference on Neural Networks, pages 757–762. IEEE.
Lovibond, S. H. and Lovibond, P. F. (1996). Manual for the depression anxiety stress scales. Psychology Foundation of Australia.
Miranda, C. A. C. and Scoppetta, O. (2018). Factorial structure of the patient health questionnaire-9 as a depression screening instrument for university students in cartagena, colombia. Psychiatry research, 269:425–429.
OMS, O. M. d. S. (2002). Relatorio mundial de saúde: Saúde mental, nova concepçáo, nova esperança.
Pintelas, E. G., Kotsilieris, T., Livieris, I. E., and Pintelas, P. (2018). A review of machine learning prediction methods for anxiety disorders. In Proceedings of the 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion, pages 8–15.
Prince, M., Patel, V., Saxena, S., Maj, M., Maselko, J., Phillips, M. R., and Rahman, A. (2007). No health without mental health. The lancet, 370(9590):859–877.
Ritchie, H. and Roser, M. (2019). Mental health. Our World in Data. https://ourworldindata.org/mental-health.
Sinha, A. (1995). Manual for sinha’s comprehensive anxiety test (scat). Agra: National Psychological Corporation.
Spielberger, C. D. (2010). State-trait anxiety inventory. The Corsini encyclopedia of psychology, pages 1–1.
Spitzer, R. L., Kroenke, K., Williams, J. B., Group, P. H. Q. P. C. S., et al. (1999). Validation and utility of a self-report version of prime-md: the phq primary care study. Jama, 282(18):1737–1744.
Srividya, M., Mohanavalli, S., and Bhalaji, N. (2018). Behavioral modeling for mental health using machine learning algorithms. Journal of medical systems, 42(5):88.
WHO et al. (2017). Depression and other common mental disorders: global health estimates. Technical report, World Health Organization.
Yuan, C. (2014). Data mining techniques with its application to the dataset of mental health of college students. In 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA), pages 391–393. IEEE.
Zivin, K., Eisenberg, D., Gollust, S. E., and Golberstein, E. (2009). Persistence of mental health problems and needs in a college student population. Journal of affective disorders, 117(3):180–185.
Alonso, S. G., de La Torre-Díez, I., Hamrioui, S., Lopez-Coronado, M., Barreno, D. C., Nozaleda, L. M., and Franco, M. (2018). Data mining algorithms and techniques in mental health: a systematic review. Journal of medical systems, 42(9):161.
Association, A. P. et al. (2014). DSM-5: Manual diagnostico e estat ístico de transtornos mentais. Artmed Editora.
Beck, A., Steer, R., and Brown, G. (1996). Manual for the beck depression inventory second edition (bdi-ii) psychological corporation. San Antonio, TX.
Bian, J., Barnes, L. E., Chen, G., and Xiong, H. (2017). Early detection of diseases using electronic health records data and covariance-regularized linear discriminant analysis. In 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pages 457–460. IEEE.
BRASIL, O. (2016). Depressao: o que voc e precisa saber.
Cohen, S., Kamarck, T., and Mermelstein, R. (1983). A global measure of perceived stress. Journal of health and social behavior, pages 385–396.
Cui Yuan (2014). Data mining techniques with its application to the dataset of mental health of college students. In 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA), pages 391–393.
da Victoria, M. S., Bravo, A., Felix, A. K., Neves, B. G., Rodrigues, C. B., Ribeiro, C. C. P., Canejo, D., Coelho, D., Sampaio, D., Esteves, I. M., et al. (2015). Níveis de ansiedade e depressao em graduandos da universidade do estado do rio de janeiro (uerj). Encontro: Revista de Psicologia, 16(25):163–175.
Day, V., McGrath, P. J., and Wojtowicz, M. (2013). Internet-based guided self-help for university students with anxiety, depression and stress: a randomized controlled clinical trial. Behaviour research and therapy, 51(7):344–351.
de Rezende, C., Abrao, C., Coelho, E., and da Silva Passos, L. (2008). Prevalencia de sintomas depressivos entre estudantes de medicina da universidade federal de uberlandia. Rev Bras Educ Med, 32(3):315–23.
Derogatis, L. (1992). Administration, scoring and procedures manual-ii (baltimore, md: Clinical psychometric research)(scl-90-r).
Devi, S., Kumar, S., and Kushwaha, G. S. (2016). An adaptive neuro fuzzy inference system for prediction of anxiety of students. In 2016 Eighth International Conference on Advanced Computational Intelligence (ICACI), pages 7–13. IEEE.
Egilmez, B., Poyraz, E., Zhou, W., Memik, G., Dinda, P., and Alshurafa, N. (2017). Ustress: Understanding college student subjective stress using wrist-based passive sensing. In 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pages 673–678. IEEE.
Han, J. and Kamber, M. (2005). Data mining : concepts and techniques. Kaufmann, San Francisco [u.a.].
Hou, Y., Xu, J., Huang, Y., and Ma, X. (2016). A big data application to predict depression in the university based on the reading habits. In 2016 3rd International Conference on Systems and Informatics (ICSAI), pages 1085–1089. IEEE.
Jensen, A. R. (1958). The maudsley personality inventory. Acta Psychologica, 14:314– 325.
Khosrowabadi, R., Quek, C., Ang, K. K., Tung, S. W., and Heijnen, M. (2011). A braincomputer interface for classifying eeg correlates of chronic mental stress. In The 2011 International Joint Conference on Neural Networks, pages 757–762. IEEE.
Lovibond, S. H. and Lovibond, P. F. (1996). Manual for the depression anxiety stress scales. Psychology Foundation of Australia.
Miranda, C. A. C. and Scoppetta, O. (2018). Factorial structure of the patient health questionnaire-9 as a depression screening instrument for university students in cartagena, colombia. Psychiatry research, 269:425–429.
OMS, O. M. d. S. (2002). Relatorio mundial de saúde: Saúde mental, nova concepçáo, nova esperança.
Pintelas, E. G., Kotsilieris, T., Livieris, I. E., and Pintelas, P. (2018). A review of machine learning prediction methods for anxiety disorders. In Proceedings of the 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion, pages 8–15.
Prince, M., Patel, V., Saxena, S., Maj, M., Maselko, J., Phillips, M. R., and Rahman, A. (2007). No health without mental health. The lancet, 370(9590):859–877.
Ritchie, H. and Roser, M. (2019). Mental health. Our World in Data. https://ourworldindata.org/mental-health.
Sinha, A. (1995). Manual for sinha’s comprehensive anxiety test (scat). Agra: National Psychological Corporation.
Spielberger, C. D. (2010). State-trait anxiety inventory. The Corsini encyclopedia of psychology, pages 1–1.
Spitzer, R. L., Kroenke, K., Williams, J. B., Group, P. H. Q. P. C. S., et al. (1999). Validation and utility of a self-report version of prime-md: the phq primary care study. Jama, 282(18):1737–1744.
Srividya, M., Mohanavalli, S., and Bhalaji, N. (2018). Behavioral modeling for mental health using machine learning algorithms. Journal of medical systems, 42(5):88.
WHO et al. (2017). Depression and other common mental disorders: global health estimates. Technical report, World Health Organization.
Yuan, C. (2014). Data mining techniques with its application to the dataset of mental health of college students. In 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA), pages 391–393. IEEE.
Zivin, K., Eisenberg, D., Gollust, S. E., and Golberstein, E. (2009). Persistence of mental health problems and needs in a college student population. Journal of affective disorders, 117(3):180–185.
Published
2020-09-15
How to Cite
FARIAS, Mariana; GUSMÃO, Rene; GUSMÃO, Cleonides.
Mineração de Dados aplicada à Saúde Mental de Estudantes Universitários: Uma Revisão Sistemática. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 20. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 49-59.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2020.11501.
