Читать книгу Semantic Web for Effective Healthcare Systems - Группа авторов - Страница 34

Much of the social media text research have been undertaken for ranking public/private sectors or products [41], for improving customer satisfaction [64], or for recommending new services of products [50]. Multi-Criteria Decision Making (MCDM) problem can be solved by using the OnSI model. Here OnSI model can be applied to extract the set of features from the given set of review documents. Ranking methods use suitable weights for each feature and the sentiment score from the sentiment analysis process. The MCDM hierarchy problem for determining the better healthcare service provider is shown in Figure 1.13.

Figure 1.13 Hierarchy of MCDM problem.

There are many MCDM methods available like SAW (simple additive weight), TOPSIS, VIKOR and so on. This chapter focuses on applying VIKOR technique for determining the better hospital based on their features. VIKOR is one of the ranking methods for optimizing the multi-response process through compromise [42]. It compares the closeness of each criterion with the alternative and derives ranking index for each criterion. The core concept of VIKOR is that ranking the criteria from the set of different alternatives in the presence of conflicting criteria [43]. Figure 1.14 shows the ranking of features for various alternatives (hospitals) under consideration.

Figure 1.14 Ranking of features by VIKOR method.

Figure 1.7 shows that the alternative H10 scored rank 1 for the features “Cost,” “Medicare,” and “Infrastructure,” the alternative H6 for the feature “Staff” and the alternative H9 for the feature “Time.” These data can be used for benchmarking by all the hospitals to improve their process.