Graduation Year

2016

Document Type

Campus Only Senior Thesis

Degree Name

Bachelor of Arts

Department

Computer Science

Reader 1

Melanie Wu

Reader 2

James Higdon

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Terms of Use for work posted in Scholarship@Claremont.

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© 2016 Shinjini V Nunna

Abstract

In order to fully examine the application of association analysis to medical data for the purpose of deriving medical diagnoses, we survey classical association analysis and approaches, the current challenges faced by medical association analysis and proposed solutions, and finally culminate this knowledge in a proposition for the application of medical association analysis to the identification of food intolerance. The field of classical association analysis has been well studied since its introduction in the seminal paper on market basket research in the 1990's. While the theory itself is relatively simple, the brute force approach is prohibitively expensive and thus, creative approaches utilizing various data structures and strategies must be explored for efficiency. Medical association analysis is a burgeoning field with various focuses, including diagnosis systems and gene analysis. There are a number of challenges faced in the field, primarily stemming from characteristics of analysis of complex, voluminous and high dimensional medical data. We examine the challenges faced in the pre-processing, analysis and post-processing phases, and corresponding solutions. Additionally, we survey proposed measures for ensuring the results of medical association analysis will hold up to medical diagnosis standards. Finally, we explore how medical association analysis can be utilized to identify food intolerances. The proposed analysis system is based upon a current method of diagnosis used by medical professionals, and seeks to eliminate manual analysis, while more efficiently and intelligently identifying interesting, and less obvious patterns between patients' food consumption and symptoms to propose a food intolerance diagnosis.

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