Developing and Validating Fuzzy-Based Trust Measures for Online Medical Diagnosis and Symptoms Analysis

Abstract

The wealth of medical information in the Web makes it expedient for non-experts to conduct their own diagnosis and healthcare assessment based on limited knowledge of signs, symptoms, and disorders. The goal of this chapter is to explain how to measure trust of websites that provide functionalities like Online Medical Diagnosis and exploration of Symptoms Analysis using fuzzy logic and soft computing techniques. Trust is qualitative and can be measured by analyzing how people interact with the websites. The interaction can be captured and analyzed by studying website logs using tools like Google analytics, click tail, etc. The chapter also provides a literature survey on the work being conducted by researchers in area of measuring website trust and tools being developed for same. It also covers archetypal techniques used for Web pattern recognition and taxonomy of trust. The main point driven by this literature survey is the frequent use of fuzzy logic in the design and implementation of trust measuring tools. This point is contrasted with the up-to-the-minute information, more specifically the authors' current work, on the use of a rule-based expert for developing trust-measuring tools.