A Review and Classification of Approaches for Dealing with Uncertainty in Multi-Criteria Decision Analysis for Healthcare Decisions

The Author(s) 2015. This article is published with open access at Springerlink.com Abstract Multi-criteria decision analysis (MCDA) is increasingly used to support decisions in healthcare involving multiple and conflicting criteria. Although uncertainty is usually carefully addressed in health eco-nomic evaluations, whether and how the different sources of uncertainty are dealt with and with what methods in MCDA is less known. The objective of this study is to review how uncertainty can be explicitly taken into account in MCDA and to discuss which approach may be appro-priate for healthcare decision makers. A literature review was conducted in the Scopus and PubMed databases. Two reviewers independently categorized studies according to research areas, the type of MCDA used, and the approach used to quantify uncertainty. Selected full text articles wer

Analysis on hybrid dominance-based rough set parameterization using private financial initiative unitary charges data

This paper evaluates the capability of the hybrid parameter reduction approach in handling private financial initiative (PFI) unitary charges data to increase the classification performance. The objective of this study is to analyse the performance of the proposed hybrid parameter reduction approach in assisting the neural network classifier to classify complex data sets that might contain uncertain and inconsistent problems. The proposed hybrid parameter reduction approach consists of several methods that will be executed during the data analysis process. Slicing technique and dominance-based rough set approach (DRSA) are the two techniques that play important roles in the proposed parameter reduction process. In order, to analyse the performance of the proposed work, the PFI data that covers all regions in Malaysia is applied in the experimental works. Besides, several standard data sets have also been used to validate the obtained results. The results reveal that the hybrid approach has successfully assisted the classifier in the classification process