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Hospital ranking is a cumbersome task, as it involves dealing with a large volume of underlying data. Rankings are usually accomplished by comparing different dimensions of quality and services. Even the quality care measurement of a hospital is multi-dimensional: It includes the experience of both clinical care and patient care. In this research, however, the authors focus on ratings based only on customer perception. A framework which consists of two stages—Stage I and Stage II—is designed. In the first stage, the model uses a rough set in a fuzzy approximation space (RSFAS) technique to classify the data; whereas in the second stage, a fuzzy soft set (FSS) technique is employed to generate the rating score. The model is employed for comparing USA hospitals by region using annual HCAHPS survey data. This article shows how ranking of the healthcare institutions can be carried out using the RSFAS (rough set in a fuzzy approximation space) and fuzzy soft set techniques.

Dengue fever is caused due to the mosquito (Aedel aegypti) bite. The symptoms of this fever are similar to other fevers such as Malaria, Chikungunya and Zika. A common sign of Dengue fever is the sharp fall of blood platelet count, amongst other a host of other confusing symptoms, which makes Dengue difficult to diagnose, especially by an inexperienced physician. The purpose of this study is to outline a decision support system (DSS) which would come to the aid of detection of Dengue fever by carrying out an analysis of AHP and fuzzy AHP (FAHP) methodology. The data of confirmed Dengue as diagnosed by a physician is picked up, examined independently using AHP and FAHP approach, the results obtained are then compared with the diagnosis report of an expert doctor. The outcome is encouraging and indicates that expert systems can be leveraged for the diagnosis of Dengue and can be a useful tool for non-expert physicians

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