Rule-Based Systems for Medical Diagnosis

Introduction

A rule-based system is a set of “if-then” like statements that uses a set of assertions. Rule based systems are used to preserve and utilize the knowledge. Rule based systems are also known as Knowledge-based expert systems. Knowledge is encoded in the form of rules and utilizes knowledge available from experts in a specified domain. Rule based expert systems (Achour, 2001) or in general expert systems were introduced in mid 1960s.

Rule-based systems differ from standard procedural or object-oriented programs in that there is no clear order in which code executes. Here, the knowledge of the expert is integrated in a set of rules, each of which encodes a small piece of the expert’s knowledge. Each rule has a left hand side and a right hand side. The left hand side contains information about certain facts and objects. One of the rules on the agenda is picked, and its right hand side is executed, and then it is removed from the agenda. Through rule based systems, intelligent decisions can be made and can trace the decision making process.

Rule-based expert systems (Aikins, 1980), use human expert knowledge to solve real world problems that normally would require human intelligence. Expert knowledge is often represented in the form of rules as well as data within the computer.

Depending upon the problem requirement, these rules and data can be recalled to solve problems. Rule-based expert systems (Aczel & Sounderpandian, 2005; Aikins, 1983) have played an important role in modern intelligent systems and their applications in strategic goal setting, planning, design, scheduling, fault monitoring, diagnosis and so on.

As already discussed, a rule-based system consists of if-then rules, a bunch of facts, and an interpreter controlling the application of the rules, given the facts. These if-then rule statements are used to formulate the conditional statements that comprise the complete knowledge base. A single if-then rule assumes the form ‘if x is A then y is B’ and the if-part of the rule ‘x is A’ is called the antecedent or premise, while the then-part of the rule ‘y is B’ is called the consequent or conclusion. There are two broad kinds of inference engines used in rule-based systems: forward chaining and backward chaining systems. In a forward chaining system, the initial facts are processed first, and keep using the rules to draw new conclusions given those facts.

In a backward chaining system, the hypothesis (or solution/goal) which is tried to reach is processed first, and keep looking for rules that would allow concluding that hypothesis. As the processing progresses, new sub goals are also set for validation. Forward chaining systems are primarily data-driven, while backward chaining systems are goal-driven.

Consider an example with the following set of if-then rules:

• Rule 1: If A and C then Y.

• Rule 2: If A and X then Z.

• Rule 3: If B then X.

• Rule 4: If Z then D.

If the task is to prove that D is true, given A and B are true. According to forward chaining, start with Rule 1 and go on down ward till a rule that fires is found. Rule 3 is the only one that fires in the first iteration. After the first iteration, it can be concluded that A, B, and X are true. The second iteration uses this valuable information. After the second iteration, Rule 2 fires adding Z is true, which in turn helps Rule 4 to fire, proving that D is true.