Document Type

Thesis

Date of Degree

2007

Degree Name

MS (Master of Science)

Degree In

Industrial Engineering

First Advisor

Andrew Kusiak

Abstract

Data mining is emerging as an important tool in many areas of research and industry. Companies and organizations are increasingly interested in applying data mining tools in order to increase the value added by their data collections systems. Nowhere is this potential more important than in the healthcare industry. As medical records systems become more standardized and commonplace, data quantity increases with much of it going unanalyzed. Data mining can begin to leverage some of this data into tools that help clinicians organize data and make decisions. These modeling techniques are explored in the following text. Through the use of clustering and classification techniques, accurate models of a dialysis patient's current status are derived.

Keywords

Data mining, clustering, anemia, hemodialysis, decision trees

Pages

x, 95 pages

Bibliography

Includes bibliographical references (pages 60-62).

Copyright

Copyright 2007 Michael Francis Bries

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