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. 2021 Mar 24;11(1):6770.
doi: 10.1038/s41598-021-86361-5.

Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation

Rafael V Veiga  1   2 , Lavinia Schuler-Faccini  3 , Giovanny V A França  4 , Roberto F S Andrade  5   6 , Maria Glória Teixeira  5   7 , Larissa C Costa  5 , Enny S Paixão  5   8 , Maria da Conceição N Costa  5   7 , Maurício L Barreto  5 , Juliane F Oliveira  5   9 , Wanderson K Oliveira  10 , Luciana L Cardim  5 , Moreno S Rodrigues  11
Affiliations

Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation

Rafael V Veiga et al. Sci Rep. .

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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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Top features in level of importance for group 2.
Figure 2
Figure 2
Flowchart describing the classification process, where CMV is positive serology for cytomegalovirus and TOXO is positive serology for Toxoplasma gondii.
See this image and copyright information in PMC

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