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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.<\/jats:p>","DOI":"10.1038\/s41598-021-86361-5","type":"journal-article","created":{"date-parts":[[2021,3,24]],"date-time":"2021-03-24T11:04:07Z","timestamp":1616583847000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation"],"prefix":"10.1038","volume":"11","author":[{"given":"Rafael V.","family":"Veiga","sequence":"first","affiliation":[]},{"given":"Lavinia","family":"Schuler-Faccini","sequence":"additional","affiliation":[]},{"given":"Giovanny V. A.","family":"Fran\u00e7a","sequence":"additional","affiliation":[]},{"given":"Roberto F. 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