期刊
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
卷 58, 期 -, 页码 46-54出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.omega.2015.03.010
关键词
Data envelopment analysis (DEA); Artificial neural networks (ANN); Training data reduction; Stratification of efficiency layers; Healthcare analytics; Organ transplant
资金
- UMass Lowell
- Division Of Graduate Education
- Direct For Education and Human Resources [0841392] Funding Source: National Science Foundation
The problem of effectively preprocessing a dataset containing a large number of performance metrics and an even larger number of records is crucial when utilizing an ANN. As such, this study proposes deploying DEA to preprocess the data to remove outliers and hence, preserve monotonicity as well as to reduce the size of the dataset used to train the ANN. The results of this novel data analytic approach, i.e. DEANN, proved that the accuracy of the ANN can be maintained while the size of the training dataset is significantly reduced. DEANN methodology is implemented via the problem of predicting the functional status of patients in organ transplant operations. The results yielded are very promising which validates the proposed method. (C) 2015 Elsevier Ltd. All rights reserved.
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