Aspects of a learned model to predict the quality of life of university students in Brazil
Resumo
Quality of life is an essential metric for evaluating the well-being of students. This work investigates the viability of a model to predict a WHOQoL-Bref answer based on other answers and the overall domain and average scores. For that, we use data from an extensive pooling done with undergraduate students in Brazil (UNICAMP), gathered between 2017 and 2018. We also discuss model types and hyperparameter effects on model evaluation metrics. Finally, we conclude that it is possible to create a model to predict the esteem question - which is the most correlated with the average domain score with the data sample available.
Referências
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Demšar, J., Curk, T., Erjavec, A., Črt Gorup, Hočevar, T., Milutinovič, M., Možina, M., Polajnar, M., Toplak, M., Starič, A., Štajdohar, M., Umek, L., Žagar, L., Žbontar, J., Žitnik, M., Zupan, B.: Orange: Data mining toolbox in python. Journal of Machine Learning Research 14, 2349-2353 (2013), http://jmlr.org/papers/v14/demsar13a.html
Division of Mental Health and Prevention of Substance Abuse World Health Organization: WHOQOL User Manual (1998)
Goecks, J., Jalili, V., Heiser, L.M., Gray, J.W.: How machine learning will transform biomedicine. Cell 181(1), 92-101 (apr 2020). https://doi.org/10.1016/j.cell.2020.03.022
Juliano, I.,, dos Santos Junior and, A.: Perfil descritivo dos estudantes de graduação na área da saúde da unicamp no ano de 2017 quanto a sexualidade, comportamento sexual, orientação sexual e identidade de gênero. In: Revista dos Trabalhos de Iniciação Científica da UNICAMP. Universidade Estadual de Campinas (nov 2019). https://doi.org/10.20396/revpibic2720191680 DOI: 10.20396/revpibic2720191680
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Lee, S., Chung, J.Y.: The machine learning-based dropout early warning system for improving the performance of dropout prediction. Applied Sciences 9(15), 3093 (jul 2019). https://doi.org/10.3390/app9153093
Macalli, M., Navarro, M., Orri, M., Tournier, M., Thiébaut, R., Côté, S.M., Tzourio, C.: A machine learning approach for predicting suicidal thoughts and behaviours among college students. Scientific Reports 11(1) (jun 2021). https://doi.org/10.1038/s41598-021-90728-z
Moutinho, I.L.D., Lucchetti, A.L.G., da Silva Ezequiel, O., Lucchetti, G.: Mental health and quality of life of brazilian medical students: Incidence, prevalence, and associated factors within two years of follow-up. Psychiatry Research 274, 306-312 (Apr 2019). https://doi.org/10.1016/j.psychres.2019.02.041
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825-2830 (2011)
Pinto, M., Marotta, N., Caracò, C., Simeone, E., Ammendolia, A., de Sire, A.: Quality of life predictors in patients with melanoma: A machine learning approach. Frontiers in Oncology 12 (mar 2022). https://doi.org/10.3389/fonc.2022.843611
Quagliato, I.,, dos Santos Junior and, A.: O estudante do PROFIS: perfil sócio-demográfico, cultural, identidade pessoal e social, espiritualidade, sexualidade, qualidade de vida, uso de álcool e outras substâncias psicoativas, saúde física e mental. In: Revista dos Trabalhos de Iniciação Científica da UNICAMP. Universidade Estadual de Campinas (nov 2019). https://doi.org/10.20396/revpibic2720192493
Sekeroglu, B., Dimililer, K., Tuncal, K.: Student performance prediction and classification using machine learning algorithms. Proceedings of the 2019 8th International Conference on Educational and Information Technology (mar 2019). http://doi.org/10.1145/3318396.3318419
Srividya, M., Mohanavalli, S., Bhalaji, N.: Behavioral modeling for mental health using machine learning algorithms. Journal of Medical Systems 42(5) (apr 2018). https://doi.org/10.1007/s10916-018-0934-5
THE WHOQOL GROUP: Development of the world health organization WHOQOL-BREF quality of life assessment. Psychological Medicine 28(3), 551-558 (may 1998). https://doi.org/10.1017/S0033291798006667
Zoller, M.A., Huber, M.F.: Benchmark and survey of automated machine learning frameworks. Journal of Artificial Intelligence Research 70 (2021) 409-472 (Apr 2019)
Batata, O., Augusto, V., Xie, X.: Caregivers burnout prediction using supervised learning. 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (oct 2018). https://doi.org/10.1109/SMC.2018.00302
Demenech, L.M., Oliveira, A.T., Neiva-Silva, L., Dumith, S.C.: Prevalence of anxiety, depression and suicidal behaviors among brazilian undergraduate students: A systematic review and meta-analysis. Journal of Affective Disorders 282, 147-159 (mar 2021). https://doi.org/10.1016/j.jad.2020.12.108
Demšar, J., Curk, T., Erjavec, A., Črt Gorup, Hočevar, T., Milutinovič, M., Možina, M., Polajnar, M., Toplak, M., Starič, A., Štajdohar, M., Umek, L., Žagar, L., Žbontar, J., Žitnik, M., Zupan, B.: Orange: Data mining toolbox in python. Journal of Machine Learning Research 14, 2349-2353 (2013), http://jmlr.org/papers/v14/demsar13a.html
Division of Mental Health and Prevention of Substance Abuse World Health Organization: WHOQOL User Manual (1998)
Goecks, J., Jalili, V., Heiser, L.M., Gray, J.W.: How machine learning will transform biomedicine. Cell 181(1), 92-101 (apr 2020). https://doi.org/10.1016/j.cell.2020.03.022
Juliano, I.,, dos Santos Junior and, A.: Perfil descritivo dos estudantes de graduação na área da saúde da unicamp no ano de 2017 quanto a sexualidade, comportamento sexual, orientação sexual e identidade de gênero. In: Revista dos Trabalhos de Iniciação Científica da UNICAMP. Universidade Estadual de Campinas (nov 2019). https://doi.org/10.20396/revpibic2720191680 DOI: 10.20396/revpibic2720191680
Kaur, M., Dhalaria, M., Sharma, P.K., Park, J.H.: Supervised machine-learning predictive analytics for national quality of life scoring. Applied Sciences 9(8), 1613 (apr 2019). https://doi.org/10.3390/app9081613
Lee, S., Chung, J.Y.: The machine learning-based dropout early warning system for improving the performance of dropout prediction. Applied Sciences 9(15), 3093 (jul 2019). https://doi.org/10.3390/app9153093
Macalli, M., Navarro, M., Orri, M., Tournier, M., Thiébaut, R., Côté, S.M., Tzourio, C.: A machine learning approach for predicting suicidal thoughts and behaviours among college students. Scientific Reports 11(1) (jun 2021). https://doi.org/10.1038/s41598-021-90728-z
Moutinho, I.L.D., Lucchetti, A.L.G., da Silva Ezequiel, O., Lucchetti, G.: Mental health and quality of life of brazilian medical students: Incidence, prevalence, and associated factors within two years of follow-up. Psychiatry Research 274, 306-312 (Apr 2019). https://doi.org/10.1016/j.psychres.2019.02.041
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825-2830 (2011)
Pinto, M., Marotta, N., Caracò, C., Simeone, E., Ammendolia, A., de Sire, A.: Quality of life predictors in patients with melanoma: A machine learning approach. Frontiers in Oncology 12 (mar 2022). https://doi.org/10.3389/fonc.2022.843611
Quagliato, I.,, dos Santos Junior and, A.: O estudante do PROFIS: perfil sócio-demográfico, cultural, identidade pessoal e social, espiritualidade, sexualidade, qualidade de vida, uso de álcool e outras substâncias psicoativas, saúde física e mental. In: Revista dos Trabalhos de Iniciação Científica da UNICAMP. Universidade Estadual de Campinas (nov 2019). https://doi.org/10.20396/revpibic2720192493
Sekeroglu, B., Dimililer, K., Tuncal, K.: Student performance prediction and classification using machine learning algorithms. Proceedings of the 2019 8th International Conference on Educational and Information Technology (mar 2019). http://doi.org/10.1145/3318396.3318419
Srividya, M., Mohanavalli, S., Bhalaji, N.: Behavioral modeling for mental health using machine learning algorithms. Journal of Medical Systems 42(5) (apr 2018). https://doi.org/10.1007/s10916-018-0934-5
THE WHOQOL GROUP: Development of the world health organization WHOQOL-BREF quality of life assessment. Psychological Medicine 28(3), 551-558 (may 1998). https://doi.org/10.1017/S0033291798006667
Zoller, M.A., Huber, M.F.: Benchmark and survey of automated machine learning frameworks. Journal of Artificial Intelligence Research 70 (2021) 409-472 (Apr 2019)
Publicado
28/11/2022
Como Citar
LOPES, Pedro Francis; SANTOS JUNIOR, Amilton; AZEVEDO, Renata Cruz Soares de; DALGALARRONDO, Paulo; FIORAVANTI, Andre Ricardo.
Aspects of a learned model to predict the quality of life of university students in Brazil. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 19. , 2022, Campinas/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 716-727.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2022.227315.
