A Hybrid Semi-Supervised Approach for Estimating the Efficient and Optimal Level of Hospitals Outputs

Abstract

Despite various supervised methods and algorithms, prediction methods do not necessarily provide the optimal values of the outputs. In this paper, an approach is proposed based on the integration of the clustering algorithm and a new mathematical programming model for predicting the efficient and optimal level of hospital outputs. In the first stage, units are evaluated using data envelopment analysis (DEA) and then, high-efficiency units are selected. In the second stage, the selected units are clustered based on the inputs of the units using the fuzzy C-means algorithm. For each unit to be estimated, the corresponding cluster is found and the closest unit to the target unit is determined. Finally, using the proposed new mathematical programming model, the optimal values are estimated based on the more efficient unit. A case study on Iranian hospitals illustrates the implementation of algorithms and methods and shows the abilities of the proposed approach.

Keywords:

• Data envelopment analysis
• fuzzy C-means
• mathematical programming
• output estimation
• semi-supervised approach