Machine Learning for Prognosis of Patients with COVID-19: An Early Days Analysis

  • José Solenir L. Figuerêdo UEFS
  • Renata F. Araújo-Calumby UEFS
  • Rodrigo T. Calumby UEFS


This work proposes a machine learning approach to predict the prognosis of patients with COVID-19. To assist in this task, a descriptive analysis and relative risk estimation were performed. In addition, the importance of variables in the perspective of machine learning algorithms was computed and discussed. The experiments were performed with large-scale nation-wide dataset from Brazil. The results reveal that the model developed was able to predict the patient's prognosis with an AUC = 0.8382. The results also point out that the chance of death is greater among patients over 60 years old, with comorbidities, and symptoms such as dyspnea and Oxygen saturation (< 95%), confirming results observed in other regions of the world.


Barbosa, V., Ferreira, H., Gomes, L., Gomes, F., Barreto, I., Monteiro, O., and Oliveira, M. (2020). Smartres - uma plataforma iot para monitoramento inteligente em saúde e sua aplicação no contexto da covid-19. In Proceedings of the 20th Brazilian Symposium on Computing Applied to Healthcare, pages 297–307, Porto Alegre, RS, Brasil. SBC.

Brazil (2021). Ministério da saúde - painel coronavírus. Accessed: April 01, 2021.

Grasselli, G., Zangrillo, A., Zanella, A., Antonelli, M., Cabrini, L., Castelli, A., Cereda, D., Coluccello, A., Foti, G., Fumagalli, R., Iotti, G., Latronico, N., Lorini, L., Merler, S., Natalini, G., Piatti, A., Ranieri, M. V., Scandroglio, A. M., Storti, E., Cecconi, M., Pesenti, A., and for the COVID-19 Lombardy ICU Network (2020). Baseline Characteristics and Outcomes of 1591 Patients Infected With SARS-CoV-2 Admitted to ICUs of the Lombardy Region, Italy. JAMA, 323(16):1574–1581.

Han, J., Pei, J., and Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.

Leung, K., Wu, J. T., Liu, D., and Leung, G. M. (2020). First-wave COVID-19 transmissibility and severity in China outside Hubei after control measures, and second-wave scenario planning: a modelling impact assessment. The Lancet, 395(10233):1382– 1393.

Mahase, E. (2021). Covid-19: What new variants are emerging and how are they being investigated? BMJ, 372.

Masood, A., Sheng, B., Li, P., Hou, X., Wei, X., Qin, J., and Feng, D. (2018). Computerassisted decision support system in pulmonary cancer detection and stage classification on ct images. Journal of Biomedical Informatics, 79:117 – 128.

Nania, R. (2021). COVID-19’s Fourth Wave: What You Need to Know Now. Accessed: August 02, 2021.

Onder, G., Rezza, G., and Brusaferro, S. (2020). Case-Fatality Rate and Characteristics of Patients Dying in Relation to COVID-19 in Italy. JAMA, 323(18):1775–1776.

Ozkaya, U., Ozturk, S., and Barstugan, M. (2020). Coronavirus (COVID-19) Classification using Deep Features Fusion and Ranking Technique.

Pollet, M. (2020). Coronavirus second wave: Which countries in europe are experiencing a fresh spike in covid-19 cases? [link]. Accessed: August 02, 2021.

Randhawa, G. S., Soltysiak, M. P. M., El Roz, H., de Souza, C. P. E., Hill, K. A., and Kari, L. (2020). Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: Covid-19 case study. PLOS ONE, 15(4):1–24.

Richardson, S., Hirsch, J. S., Narasimhan, M., Crawford, J. M., McGinn, T., Davidson, K. W., , and the Northwell COVID-19 Research Consortium (2020). Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. JAMA, 323(20):2052–2059.

Silva, S., Simozo, F., Junior, L. M., and Tinós, R. (2020). Uso de redes neurais convolucionais para identificar displasia cortical focal em pacientes com epilepsia refratária. In Anais do XVII Encontro Nacional de Inteligência Artificial e Computacional, pages 211–221, Porto Alegre, RS, Brasil. SBC.

Sousa, I., Vellasco, M., and Silva, E. (2020). Classificações explicáveis para imagens de células infectadas por malária. In Anais do XVII Encontro Nacional de Inteligência Artificial e Computacional, pages 47–57, Porto Alegre, RS, Brasil. SBC.

Souza, F. S. H., Hojo-Souza, N. S., Santos, E. B., Silva, C. M., and Guidoni, D. L. (2020). Predicting the disease outcome in covid-19 positive patients through machine learning: a retrospective cohort study with brazilian data. medRxiv.

Wang, C., Horby, P. W., Hayden, F. G., and Gao, G. F. (2020a). A novel coronavirus outbreak of global health concern. The Lancet, 395(10223):470–473.

Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., Wang, B., Xiang, H., Cheng, Z., Xiong, Y., Zhao, Y., Li, Y., Wang, X., and Peng, Z. (2020b). Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus–Infected Pneumonia in Wuhan, China. JAMA, 323(11):1061–1069.

White, D. B. and Lo, B. (2020). A Framework for Rationing Ventilators and Critical Care Beds During the COVID-19 Pandemic. JAMA, 323(18):1773–1774.

WHO (2021a). Tracking sars-cov-2 variants. Accessed: August 02, 2021.

WHO (2021b). Who coronavirus (covid-19) dashboard. Accessed: August 01, 2021.

Xie, J., Covassin, N., Fan, Z., Singh, P., Gao, W., Li, G., Kara, T., and Somers, V. K. (2020). Association Between Hypoxemia and Mortality in Patients With COVID-19. Mayo Clinic Proceedings, 95(6):1138–1147.

Yan, L., Zhang, H.-T., Goncalves, J., Xiao, Y., Wang, M., Guo, Y., Sun, C., Tang, X., Jing, L., Zhang, M., Huang, X., Xiao, Y., Cao, H., Chen, Y., Ren, T., Wang, F., Xiao, Y., Huang, S., Tan, X., Huang, N., Jiao, B., Cheng, C., Zhang, Y., Luo, A., Mombaerts, L., Jin, J., Cao, Z., Li, S., Xu, H., and Yuan, Y. (2020). An interpretable mortality prediction model for COVID-19 patients. Nature Machine Intelligence, 2(5):283–288.

Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., Liu, Z., Xiang, J., Wang, Y., Song, B., Gu, X., Guan, L., Wei, Y., Li, H., Wu, X., Xu, J., Tu, S., Zhang, Y., Chen, H., and Cao, B. (2020). Clinical course and risk factors for mortality of adult inpatients with COVID19 in Wuhan, China: a retrospective cohort study. The Lancet, 395(10229):1054–1062.
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FIGUERÊDO, José Solenir L.; ARAÚJO-CALUMBY, Renata F.; CALUMBY, Rodrigo T.. Machine Learning for Prognosis of Patients with COVID-19: An Early Days Analysis. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 18. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 59-70. DOI:

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