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research is twofold: (1) we show how DEA can be used as a methodology to screen training cases, where forecasting models are subject to the managerial monotonicity assumption; and (2) we show how ANNs can be applied to forecast the number of employees in the healthcare industry Empirical studies are conducted to compare the predictive performance of ANNs ith learning from a set of DEA-based selected training cases, DEA-based rejected training cases, and a combined set of DEA-based selected and rejected training cases We use the publicly available data from healthcare facilities in Pennsylvania to learn about and predict the number of employees based on a set of predictor variables DEA was a technique introduced for comparing ef ciencies of decision making units (DMUs) The basic ratio DEA model seeks to determine a subset of k DMUs that determine the envelopment surface when all k DMUs consist of m inputs and s outputs The envelopment surface was determined by solving k linear programming models (one for each DMU), where all k DMUs appear in the constraints of the linear programming model ANNs have been applied to numerous nonparametric and nonlinear classi cation and forecasting problems In an ANN model, a neuron is an elemental processing unit that forms part of a larger network There are two basic types of ANNs: a single-layer (of connections) network and a double-layer (of connections) network A single-layer network, using the perceptron convergence procedure, represents a linear forecasting model A modi cation of the perceptron convergence procedure can be used to minimize the mean-square error between the actual and desired outputs in a two-layer network, which yields a nonlinear, multivariate forecasting model The backpropagation learning algorithm, most commonly used to train multilayer networks, implements a gradient search to minimize the squared error between realized and desired outputs Figure 33 shows a three-layer network that can be used for multivariate forecasting The number of input layer nodes corresponds to the number of independent variables describing the data The number of nodes in the hidden layer determines the complexity of the forecasting model and needs to be empirically determined to best suit the data being considered While larger networks tend to over t the data, too few hidden layer nodes can hinder learning of an adequate separation region Although having more than one hidden layer provides no advantage in terms of forecasting accuracy, it can in certain cases provide for faster learning The network is initialized with small random values for the weights, and the backpropagation learning procedure is used to update the weights as the data are iteratively presented to the input-layer neurons The weights are updated until some predetermined criterion is satis ed The number of hidden layer neurons is chosen as twice the number of data inputs, a commonly used heuristic in the literature Heuristics of the more the better could be used as a guide to select the number of hidden nodes in an ANN Furthermore, a network with a higher number of hidden nodes can always be considered as a special case of the network, with additional nodes (in the case of an ANN with a higher number of hidden nodes) having connection weights taking values equal to zero.

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