## 2 Related Work  
A lot of research efforts have been made to deal with the problem of overcrowding with regard to the ED of the hospital [22; 23; 24; 25]. To deal with this problem, inter-hospital transfers have been proposed as a possible solution [26]. However, overcrowding is also a problem in the OPD and equally affects patient satisfaction, which is affected by the efficiency of the services rendered. Efficient provision of health services encompasses such issues as waiting time to consultation, duration of consultation, timely response to emergencies, quick drug dispensation, and timely and accurate diagnosis [27; 28]. The challenge in dealing with excess demand results from the challenges in predicting patient flow in the OPD. Patient load forecasting involves predicting patient loads for future time periods. Depending on time horizons, patient load prediction can be categorized into: Long-range forecasting, medium-range forecasting, short-range forecasting and real-time or very short-term forecasting. Accurate patient flow predictions may aide in improving hospital management efficiency. Traditional methods have been applied in forecasting hospital visits. In [29], [PERSON] and [PERSON] explored the problem of predicting ED patient volume, length of stay and acuity. They studied five models, namely raw observations, moving averages, mean values with moving averages, seasonal indicators with moving averages and auto-regressive integrated moving averages (ARIMAs). It was discovered in this study that simpler models performed best. In [30], [PERSON] et al. explore the problem of short-range forecasting of patient volume. A general linear model (GLM) is formulated that can be applied for short-range forecasting of patient volume. In [31], [PERSON] et al. developed equations for predicting daily patient volumes via stepwise linear regression analysis. In [32], [PERSON] and [PERSON] developed a trimmed mean seasonal model for the expected number of daily patient visits to an ED. In [33], [PERSON] et al. constructed a first order cyclical regression model with fixed-width sine and cosine harmonics as the seasonal component and a hierarchical model with a scalable Gaussian function as the seasonal component for ED daily respiratory chief complaints. In [34], [PERSON] et al. formulated a mathematical model for forecasting ED censuses. In [35], [PERSON] et al. investigated the performance of a general linear regression model formed with 11 dummy variables in forecasting monthly patient admissions. In [36], [PERSON] et al. forecasted patient arrivals to an accident and ED via a structural time series model. In [37], [PERSON] et al. studied the problem of predicting daily patient numbers for a regional medical center via the application of time series analysis. [PERSON] et al. applied time series analyses; specifically, they applied best-fitting models of ARIMA and linear regression to various prediction models and compared the results using error statistics. Their work aimed at forecasting censuses in neonatal intensive care units (NICU) [38]. Other interesting studies relatingto predicting patient flow in ED have been reported in [39; 40; 41; 42]. While these traditional techniques are popular in forecasting hospital visits, they are not good at dealing with complexity in hospital visits data. Artificial Intelligence techniques, e.g., artificial neural networks (ANNs), form another approach for hospital visit forecasting [43]. However, ANN is not as prevalent as the traditional methods in this field. In [44], [PERSON] et al. explored the performance comparison of exponential smoothing, seasonal ARIMA, ANN and time series regression in daily ED patient volume prediction with linear regression. Their results show that the former four methods did not perform consistently in forecasting with a sample, although they all performed better in sample fitting. In [45], [PERSON] and [PERSON] modeled the number of outpatient visits by ANN using different activation functions. In [46], [PERSON] et al.