Analysis of healthcare predictions in Florianópolis
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1338Keywords:
time series, prediction, primary health care, Medical careAbstract
Objective: To compare time series models in predicting monthly individual visits in Florianópolis in 2024. Methods: Using public data on visits from 2019 to 2023 from the Brazilian Ministry of Health, applied in the ARIMA, SARIMA, Stacking and Holt-Winters models. The comparison was based on error metrics. Results: SARIMA showed greater accuracy, while ARIMA generated constant prediction for all months, although its error metrics were similar to SARIMA. Conclusions: The application of time series models is useful for public health planning, although differences between models indicate limitations. These techniques can optimize resources and improve the quality of care, but additional studies are needed to deepen the analyzes and improve predictions.
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