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

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.

Machine learning and deep learning techniques to support the clinical diagnosis of arboviral diseases: A systematic review

“…On the other hand, over-complex trees do not generalise the data well, often presenting overfitting (or underfitting), and are prone to errors with relatively small number of samples for training. The sample includes eleven Decision Tree models - [40], [55], [49], [51], [56], [42], [43], [44], [45], [50] and [48].…”

“…Multiple classification trees are obtained from bootstrap samples in order to calculate the final majority classification. The SLR sample includes three Random Forest models - [46], [50] and [48]. As Random Forest models combine different Decision Trees, their results are not as easy to understand as a Decision Tree and are also more expensive computationally.…”

“…As the amount of data used in this experiment is greater (1109 samples) than the binary classification, the Gradient Boost obtained a better performance. However, Veiga et al [48] did not provide details on the proportion of data in each class. As such, it is not possible to analyse whether any data imbalance impacted the models performance.…”

“…It is worth noting that a number of studies used cross-validation techniques to validate and test their models. [40], [43], [45], [56] [50] and [48] used k-fold cross-validation with k = 10; and [55] also used k-fold but with k = 5. Lee et al [51] was the only study that applied the leave-one-out (LOO) cross-validation.…”

“…Additionally, there is also textual data provided by the health professional when registering the newborns' information in the system such as reports, descriptions and other possible observations. Veiga et al [48] separated the records into two groups where Group 1 contained only clinical and non-clinical data (272 records), and Group 2 containing clinical, non-clinical and complementary textual notes (1,109 records). The most frequent terms presented in the notes were used to assist the classification.…”