학술논문
Live Births Prediction using Legendre Memory Unit: A Case Study for the Health Regions of Goiás
Document Type
Conference
Author
Source
2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS) CBMS Computer-Based Medical Systems (CBMS), 2023 IEEE 36th International Symposium on. :329-334 Jun, 2023
Subject
Language
ISSN
2372-9198
Abstract
The use of forecasting models is becoming even more common in healthcare and administration applications because it can be a reliable decision support tool. Live birth rate is a health index that is directly linked with maternal and newborn health and its prediction can assist health managers to anticipate resources destined for obstetric and pediatric services. Thus, the objective of this work is to forecast the number of live births in the state of Golás (Brazil) for a 24-month horizon, providing useful information to support the planning and implementation of public policies. The model suggested is the Legendre Memory Unit (LMU) which is applied to data provided by the information system on live births of the information department of the single health system (SINASC-DATASUS). The dataset is composed of 252 monthly records of the number of live births for the 18 health regions of Golás. The results were measured in prediction ability by Mean Absolute Percentual Error (MAPE) and Mean Absolute Error (MAE). The average MAPE and MAE were 6.4614 and 19.9136, respectively.