Application of Markov Chains and Artificial Neural Networks in the prediction of Hepatitis Cases in the southern states of Brazil

Viral hepatitis is a disease caused by five known types of virus and can be chronic or acute. This study collected historical data on hepatitis in the southern states of Brazil and applied Markov Chains as an input parameter for predicting cases, using Artificial Neural Networks (ANN). A comparison was then made between the method that includes the two methodologies and the prediction made by ANN using only the data from the historical series. The mean absolute error (MAE) and mean absolute percentage error (MAPE) were calculated to determine the best forecasting model for each state. It was not possible to determine a configuration that simultaneously presented the best MAE and MAPE values for each state, but it was found that the lowest errors were obtained by using Markov Chains as an information generator for the ANN models, with a MAPE of 4.45% using the Levenberg-Marquardt training algorithm, with a delay equal to 3 and a number of neurons equal to 60.