Machine Learning for Neonatal Mortality Risk Assessment: A Case Study Using Public Health Data from Sao Paulo
Beluzo, C. E.; Alves, L. C.; Bresan, R. C.; Arruda, N. M.; Sovat, R. B.; Carvalho, T.
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Infant mortality is a reflection of a complex combination of biological, socioeconomic and health care factors that require various data sources for a thorough analysis. Consequently, the use of specialized tools and techniques to deal with a large volume of data is extremely helpful. Machine learning has been applied to solve problems from many domains and presents great potential for the proposed problem, which would be an innovation in Brazilian reality. In this paper, an innovative method is proposed to perform a neonatal death risk assessment using computer vision techniques. Using mother, pregnancy care and child at birth features, from a dataset containing neonatal samples from Sao Paulo city public health data, the proposed method encodes images features and uses a custom convolutional neural network architecture to classification. Experiments show that the method is able to detect death samples with accuracy of 90.61%.
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