Modeling and predictionof hypertension in Komfo Anokye Teaching Hospital (KATH), Ghana

Agyei Helena Lartey1, Jianming Wang1, Philip Lartey3, James Agyei2, Alex Agyei2, Janet Sintim Aboagye2

Abstract

 

Accepted 18th June, 2020

To explore the temporal trends of hypertension in a Ghana population and to predict future values, which will, in turn, help control and reduce the risk of hypertension-related health events. We enrolled 108,100 cases with essential hypertension from January 2015 to December 2019 at the Komfo Anokye Teaching Hospital (KATH), Ghana. The Box-Jenkins Autoregressive Integrated Moving Average model (ARIMA) was used to identify trends and forecast data from a specified time series. The root mean square error (RMSE), Q-statistic, residual variance (RV), and Akaike’s information criteria (AIC) were used to assess the performance of the model. The most optimal model was ARIMA(1, 1, 0) with RV(7061), RMSE(82.6155), AIC(693.48), Q-value(19.187), parameter(-0.4034) and constant(188.6501). The best fitting model was Yt = (1-0.4034)Yt-1 -0.4034Yt-2 +1801.6670. The model estimated an increase in hypertension cases for the next period, which was a critical input in managerial and administrative decision making. The forecast was accurate enough to allow for better planning and control than could be accomplished without the forecast.

Keywords:Hypertension, Forecast, ARIMA, RMSE, RV, AIC

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