A dual hybrid forecasting model for support of decision making in healthcare management
Forecasting of time series data such as fertility, morbidity and mortality rates is important for healthcare managers as these data serve as health indicators of a society. Accurate forecasting of these data based on past values helps the healthcare managers in taking appropriate decisions for avoiding possible calamity situations. Healthcare time series data consist of complex linear and nonlinear patterns and it may be difficult to obtain high forecasting accuracy rates using only linear or neural network models. In this paper, we present a dual hybrid forecasting model based on soft computing technology. The proposed method makes use of a combination of linear regression, neural network and fuzzy models. The inputs to the fuzzy model are the forecast values of healthcare time series data. Based on a set of rules, the fuzzy model yields a qualitative output which is useful for decision making in healthcare management.
dual hybrid forecasting models decision making neural network soft computing technology linear regression fuzzy logic