Team: Lais Baroni (CEFET/RJ), Lucas Scoralick (CEFET/RJ), Augusto Reis (CEFET/RJ), Kele Belloze (CEFET/RJ), Marcel Pedroso (Fiocruz), Ronaldo Alves (Fiocruz), Cristiano Boccolini (Fiocruz), Patricia Boccolini (UNIFASE), Eduardo Ogasawara (CEFET/RJ)
Abstract: Epidemiology is important in public health because it studies the health-disease-care process in human populations. One of the main focuses of this science is to identify the determinants factors in the health situation of populations once it is understood that health-related anomalies are not randomly distributed among people. This understanding brings up the necessity of considering the particularities of each place and the observation of the regularity of diseases in a population context. In this work, we present \acf{cca} for the discovery of associations between \acf{hi} and \acf{hd} in situations of anomalies at the \ac{hi}. \ac{cca} uses time series concepts, anomaly detection, and data distribution between classes for studying \ac{hd} under expected conditions and comparing them to the anomalies conditions indicated by the anomaly detection in the \ac{hi}. \ac{cca} is evaluated in a neonatal mortality database in health facilities in Rio de Janeiro (RJ, Brazil). The results show that \ac{cca} can reveal important associations between the health condition and social, economic, and cultural characteristics of the population in different scales.
Acknowledgments: The authors thank CNPq, CAPES, and FAPERJ for partially sponsoring this research.
Experimental Evaluation:
The data and codes used to perform the experimental evaluation are available in https://github.com/cefet-rj-dal/cca