Title

Selecting Critical Clinical Features for Heart Diseases Diagnosis with a Real-coded Genetic Algorithm

Document Type

Article

Publication Date

3-2008

Publication Title

Applied Soft Computing

Volume

8

Issue

2

First page number:

1105

Last page number:

1111

Abstract

In clinic, normally a lot of diagnostic features are recorded from a patient for a certain disease. It will be beneficial for the prompt and correct diagnosis of the disease by selecting the important and relevant features and discarding those irrelevant and redundant ones. In this paper, a real-coded genetic algorithm (GA)-based system is proposed to select the critical clinical features essential to the heart diseases diagnosis. The heart disease database used in this study includes 352 cases, and 40 diagnostic features were recorded for each case. Using the proposed genetic algorithm, 24 critical features have been identified, and their corresponding diagnosis weights for each heart disease of interest have been determined. The critical diagnostic features and their clinic meanings are in sound agreement with those used by the physicians in making their clinic decisions.

Keywords

Genetic algorithms; Diagnosis; Differential; Machine learning; Medical care--Decision making

Disciplines

Analytical, Diagnostic and Therapeutic Techniques and Equipment | Electrical and Computer Engineering | Health Information Technology | Other Biomedical Engineering and Bioengineering

Language

English

Permissions

Use Find in Your Library, contact the author, or interlibrary loan to garner a copy of the item. Publisher policy does not allow archiving the final published version. If a post-print (author's peer-reviewed manuscript) is allowed and available, or publisher policy changes, the item will be deposited.

Identifier

DOI: 10.1016/j.asoc.2007.05.017

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