Converting a rule-based expert system into a belief network

Converting a Rule-based Expert System into a Belief Network

Converting a rule-based expert system into a belief network.

The theory of belief networks offers a relatively new approach for dealing with uncertain information in knowledge-based (expert) systems. In contrast with the heuristic techniques for reasoning with uncertainty employed in many rule-based expert systems, the theory of belief networks is mathematically sound, based on techniques from probability theory. It therefore seems attractive to convert ...

A Belief Rule Based Expert System to Diagnose Measles under Uncertainty

Measles is a highly infectious child disease that causes serious complications and death worldwide. Measles is generally diagnosed from its signs and symptoms by a physician, which cannot be measured with 100% certainty during the diagnosis process. Consequently, the traditional way of diagnosing measles from its signs and symptoms lacks the accuracy. Therefore, a belief rule-based inference me...

A Belief Rule Based Expert System to Assess Autism under Uncertainty

Autism is one of the most common neurological disorders found in children, resulting disabilities, which continue until adulthood. The accurate assessment of autism is considered as a challenging clinical decision making problem because of the presence of various types of uncertainties that exist with its factors such as social interaction, communication and behavior. These factors cannot be me...

A Fuzzy Rule-based Expert System for the Prognosis of the Risk of Development of the Breast Cancer

Soft Computing techniques play an important role for decision in applications with imprecise and uncertain knowledge. The application of soft computing disciplines is rapidly emerging for the diagnosis and prognosis in medical applications. Between various soft computing techniques, fuzzy expert system takes advantage of fuzzy set theory to provide computing with uncertain words. In a fuzzy exp...