Date of Award


Degree Type


Degree Name

Master of Science in Statistics


Computer Science and Statistics

First Advisor

Colleen Kelly


Diagnosing Lyme disease has been problematic since its first recognition in 1975. An assortment of problems, including clinical symptoms that mimic several other diseases, and lack of an accurate laboratory test, have hindered diagnosis. Overdiagnosis and misdiagnosis may result. This thesis seeks to improve the accuracy of diagnosing Lyme disease by creating an expert system.

The type of expert system developed in this thesis will be a probabilistic Bayesian belief network. The network consists of nodes which represent diagnostic variables and links between nodes which represent the probabilistic influence one node has on another. Much is known about Lyme disease, its transmission, and the diagnostic symptoms that are associated with the disease. This information about variables is incorporated into the network through a literature search. Initial estimates of these variables were determined to initialize the system with a priori values. To test the system, data were collected on a number of patients who presented symptoms consistent with Lyme disease. The system's classification will be compared to the patients classification based on serological results and methods for improving the system's accuracy are discussed.



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