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Abstract
Zika virus was responsible for the microcephaly epidemic in Brazil which began in October 2015 and brought great challenges to the scientific community and health professionals in terms of diagnosis and classification. Due to the difficulties in correctly identifying Zika cases, it is necessary to develop an automatic procedure to classify the probability of a CZS case from the clinical data. This work presents a machine learning algorithm capable of achieving this from structured and unstructured available data. The proposed algorithm reached 83% accuracy with textual information in medical records and image reports and 76% accuracy in classifying data without textual information. Therefore, the proposed algorithm has the potential to classify CZS cases in order to clarify the real effects of this epidemic, as well as to contribute to health surveillance in monitoring possible future epidemics.
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1 Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Center of Data and Knowledge Integration for Health (CIDACS), Salvador, Brazil (GRID:grid.418068.3) (ISNI:0000 0001 0723 0931); Universidade Federal da Bahia, Instituto de Ciências da Saúde, Salvador, Brazil (GRID:grid.8399.b) (ISNI:0000 0004 0372 8259)
2 Universidade Federal do Rio Grande do Sul, Rio Grande do Sul, Brazil (GRID:grid.8532.c) (ISNI:0000 0001 2200 7498)
3 Secretariat of Health Surveillance, Ministry of Health, Brasilia, Brazil (GRID:grid.414596.b) (ISNI:0000 0004 0602 9808)
4 Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Center of Data and Knowledge Integration for Health (CIDACS), Salvador, Brazil (GRID:grid.418068.3) (ISNI:0000 0001 0723 0931); Universidade Federal da Bahia, Instituto de Física, Salvador, Brazil (GRID:grid.8399.b) (ISNI:0000 0004 0372 8259)
5 Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Center of Data and Knowledge Integration for Health (CIDACS), Salvador, Brazil (GRID:grid.418068.3) (ISNI:0000 0001 0723 0931); Universidade Federal da Bahia, Instituto de Saúde Coletiva, Salvador, Brazil (GRID:grid.8399.b) (ISNI:0000 0004 0372 8259)
6 Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Center of Data and Knowledge Integration for Health (CIDACS), Salvador, Brazil (GRID:grid.418068.3) (ISNI:0000 0001 0723 0931)
7 Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Center of Data and Knowledge Integration for Health (CIDACS), Salvador, Brazil (GRID:grid.418068.3) (ISNI:0000 0001 0723 0931); London School of Hygiene and Tropical Medicine, London, United Kingdom (GRID:grid.8991.9) (ISNI:0000 0004 0425 469X)
8 Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Center of Data and Knowledge Integration for Health (CIDACS), Salvador, Brazil (GRID:grid.418068.3) (ISNI:0000 0001 0723 0931); Centre of Mathematics of the University of Porto (CMUP), Department of Mathematics, Porto, Portugal (GRID:grid.5808.5) (ISNI:0000 0001 1503 7226)
9 Hospital das Forças Armadas, Ministério da Defesa, Brasília, Brazil (GRID:grid.456757.2) (ISNI:0000 0004 0615 8060)
10 Fundação Oswaldo Cruz, Porto Velho, Brazil (GRID:grid.418068.3) (ISNI:0000 0001 0723 0931)