Classification of Newborns with Congenital Syndrome Associated with Zika Virus Infection Using Machine Learning

  • Érika G. de Assis Pontifícia Universidade Católica de Minas Gerais
  • Luis E. Zárate Pontifícia Universidade Católica de Minas Gerais
  • Cristiane N. Nobre Pontifícia Universidade Católica de Minas Gerais


Due to evidence that Zika virus (ZIKV) infection during pregnancy caused congenital brain anomalies, including microcephaly, in 2016 the WHO declared this disease a worldwide public health problem. The objective of this work is to identify the most important characteristics for the diagnosis of children with congenital syndrome due to ZIKV virus infection. We applied machine learning algorithms to RESP-Microcephaly, a database from the Brazilian Ministry of Health that records suspected cases of congenital abnormalities. At the end of the process, the most relevant characteristics were: weight, age of the pregnant woman, length, head circumference and region where the mother lives. This information is very significant as it is in agreement with the literature that associates these attributes with critical factors for the occurrence of congenital infection.
Palavras-chave: Zika Vírus, Congenital Syndrome, Machine Learning


Almeida, I., Ramos, C., Rodrigues, D., Sousa, A. d., Nascimento, M. C. d., Silva, M. d., Batista, F., Santos, J. P. d., Oliveira, R., Soares, F., Xavier, S., and Carvalho-Costa, F. Clinical and epidemiological aspects of microcephaly in the state of Piauí, northeastern Brazil, 2015-20166. Jornal de Pediatria vol. 95, pp. 466 – 474, 08, 2019.

Brasil, da Saúde, M., de Vigilância em Saúde, S., and de Atenção à Saúde, S. Orientações integradas de vigilância e atenção à saúde no âmbito da emergência de saúde pública de importância nacional, 2017.

Herber, S., Silva, A. A., Sanseverino, M. T. V., Friedrich, L., Ranieri, T. M., Favreto, C., Fraga,L. R., Terra, A. P., Schwartz, I. V., and Schuler-Faccini, L. Prevalence and causes of congenital microcephaly in the absence of a zika virus outbreak in southern brazil. Jornal de Pediatria 95 (5): 600 – 606, 2019.

Mitchell, P. K., y Teran-Romero, L. M., Biggerstaff, B. J., Delorey, M. J., Aubry, M., Cao-Lormeau, V.-M., Lozier, M. J., Cauchemez, S., and Johansson, M. A. Reassessing serosurvey-based estimates of the symptomatic proportion of zika virus infections. American Journal of Epidemiology vol. 188, pp. 206–213, 2019.

Nunes, M., Carlini, C., Marinowic, D., Kalil N, F., Fiori, H., Scotta, M., Zanella, P. . A., Soder, R., and Costa, J. Microcephaly and Zika virus: a clinical and epidemiological analysis of the current outbreak in Brazil,. Jornal de Pediatria vol. 92, pp. 230 – 240, 06, 2016.

Prata-Barbosa, A. M., Melo, M., Guastavino, A. B., and Cunha, A. J. L. A. d. Effects of Zika infection on growth,. Jornal de Pediatria vol. 95, pp. S30 – S41, 00, 2019.

Ribeiro, I., MR, A., Silva, J., ZM, S., Costa, M., MACS, V., FMA, B., H, G., MY, W., and E, S. Microcefalia no Piauí, Brasil: estudo descritivo durante a epidemia do vírus Zika, 2015-2016. Epidemiologia e Serviço de Saúde vol. 27, pp. 11, 00, 2018.
Como Citar

Selecione um Formato
ASSIS, Érika G. de; ZÁRATE, Luis E.; NOBRE, Cristiane N.. Classification of Newborns with Congenital Syndrome Associated with Zika Virus Infection Using Machine Learning. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 9. , 2021, Rio de Janeiro. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 57-64. ISSN 2763-8944. DOI: