Towards hybrid artificial neural network model in predicting long term total healthcare expenditure in Rwanda

Abstract For a sustainable Healthcare expenditure policy, it is important to have accurate forecasts to avoid any sort of uncertainty about the rise in Healthcare expenditures. The Auto Regressive Integrated Moving Average (ARIMA) model and the Artificial Neural Networks (ANNs) models have got attention and have been the preferred model to forecast Healthcare expenditures for decades. However, it has been found that each of the two models has weaknesses in capturing linear and nonlinear relationships in data. It is with this background that, we proposed a hybrid model, which differs in combining the advantages of Auto Regressive Integrated Moving Average and Artificial Neural Networks. The hybrid model was tested on sets of Rwanda total Healthcare expenditure actual data, and the empirical results of the forecasted values in 2027 are 552,881.33 million Rwandan francs. Our results showed the effectiveness of the hybrid model which has a higher prediction accuracy as compared to the existing models. For policymakers to better plan for future resources and estimate the amount of the budget allocated to Healthcare, there is a need of accurate estimation of Healthcare expenditure as it impact government policy and planning.