Document Type

Thesis

Date of Award

10-31-1991

Degree Name

Master of Science in Electrical Engineering - (M.S.)

Department

Electrical Engineering

First Advisor

Peter Engler

Second Advisor

Stanley Martin Dunn

Third Advisor

Stanley S. Reisman

Abstract

The intriguing words for the nineteen nineties are "Expert Systems", and "Artificial Intelligence". To a layman these terms conjure up thoughts of spaceships and futuristic beings. In reality they exemplify man's capability to harness technology.

Expert systems are computer software programs that make use of previously accumulated knowledge to assist in solving complex problems.

In recent years the practice of medicine has changed dramatically from a fundamentally individually deductive approach to an Artificial Intelligence based science. The modern physician no longer draws conclusions simply by examining the patient. Today the physician employs sophisticated computer based diagnostic tools to generate a diagnosis.

Clearly, Expert Systems are a means of minimizing a high degree of uncertainty. Margins of error are greatly reduced so that the majority of diagnostic conclusions have a greater percentage of accuracy. Bayesian theory is commonly applied to generate conditional probabilities which take into account the frequency of occurrence of events in specific domains. Bayesian Theory has been employed in the development of a new Expert System called CANMOL.

In theory CANMOL could be applied as a diagnostic tool to a broad range of medical applications. By altering the knowledge base the application would change, while the program itself would remain consistent.

For the purpose of this thesis CANMOL will be employed to: 1) predict the occurrence of Breast Cancer, and 2) the probability of complications arising from extraction or retention of the third molar. CANMOL's flexibility is exhibited by its application to two distinct and unrelated areas within the sphere of medicine.

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.