Background: Medical practice is usually performed in a context of uncertainty, where expert knowledge has shown to be efficient in the decision-making process. Objective: The aim of this study was to develop and validate a fuzzy logic-based model to predict cardiac surgery mortality risk. Methods: Four hundred and fifty patients undergoing cardiac surgery were prospectively included in the stud...

Fuzzy logic and fuzzy set theory provide an important framework for representing and managing imprecision and uncertainty in medical expert systems, but the need remains to optimize such systems to enhance performance. This paper presents a general technique for optimizing fuzzy models in fuzzy expert systems (FES’s) by simulated annealing (SA) and N -dimensional hill climbing simplex method. T...

Before beginning the research that led to "Probabilistic logic" [11 ], I had participated with Richard Duda, Peter Hart, and Georgia Sutherland on the PROSPECTOR project [3]. There, we used Bayes' rule (with some assumptions about conditional independence) to deduce the probabilities of hypotheses about ore deposits given (sometimes uncertain) geologic evidence collected in the field [4]. At th...

This article presents the development of the knowledge base and inference motor for an Automated Management System for developing Expert Systems and Fuzzy Classifier (SAGSECD). SAGSECD is a tool that can be used for any user with basics knowledge in the expert systems or fuzzy classifiers area for designing and implementing this kind of systems to solve several problems or situations that requi...

Efficient use of enterprise resource planning systems is the only way to achieve competitive advantage in many industries. However, many reports indicate high failure rate of ERP implementation projects and the lack of access to benefits and advantages that enterprises have expected from ERP deployment. Managers are concerned about being ready to deploy such a system. Despite its enormous cost,...

Most fuzzy controllers and fuzzy expert systems must predefine membership functions and fuzzy inference rules to map numeric data into linguistic variable terms and to make fuzzy reasoning work. In this paper, we propose a general learning method as a framework for automatically deriving membership functions and fuzzy if-then rules from a set of given training examples to rapidly build a protot...

This paper expresses the prominent features of the fuzzy expert system by applying the algorithm Fuzzy Assessment Methodology using K ratio. To diagnosis the diabetes Fuzzy Assessment Methodology using k ratio is developed. Fuzzy Expert System consists of following elements such as Fuzzification interface, Fuzzy Assessment Methodology using K ratio and Defuzzification interface. The crisp value...

One of the superior capabilities of fuzzy systems is that they can use the information expressed in a linguistic pattern. Though most fuzzy systems, have been formed to emulate human decision making behaviour, the linguistic information stated by an expert may not be precise or that it is difficult for the expert to articulate the accumulated knowledge to encompass all circumstances. Hence, it ...