Adelaide Research & Scholarship
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https://hdl.handle.net/2440/19356
| Type: | Thesis |
| Title: | Intelligent techniques for the diagnosis of coronary artery disease / Ravi Jain. |
| Author: | Jain, Ravi, 1967- |
| Issue Date: | 1998 |
| School/Discipline: | Dept. of Applied Mathematics |
| Abstract: | This thesis proposes a genetic-programming-based classifier system for the diagnosis of coronary artery disease. Based on genetic programming, a software system called Evolutionary Pre-Processor has been developed as a new method for the automatic extraction of non-linear features for supervised classification. Two different hybrid intelligent system techniques are presented; fuzzy systems integrated with genetic algorithms and genetic algorithms combined with back-propagation algorithms. All approaches were tested on a real-world problem of coronary artery disease data. |
| Dissertation Note: | Thesis (Ph.D.)--University of Adelaide, Dept. of Applied Mathematics, 1998 |
| Subject: | Genetic programming (Computer science) Coronary heart disease Mathematical models. Fuzzy logic. Neural networks (Computer science) Genetic algorithms. |
| Description: | Bibliography: leaves 179-190. xii, 189 leaves : ill. ; 30 cm. |
| Provenance: | This electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exception. If you are the author of this thesis and do not wish it to be made publicly available or If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at: http://www.adelaide.edu.au/legals. Copyright material removed from digital thesis. See print copy in University of Adelaide Library for full text. |
| Appears in Collections: | Research Theses |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 01front.pdf | 180.65 kB | Adobe PDF | View/Open | |
| 02whole.pdf | 8.68 MB | Adobe PDF | View/Open | |
| Permissions Restricted Access | Library staff access only | 246.94 kB | Adobe PDF | View/Open |
| Restricted Restricted Access | Library staff access only | 10.53 MB | Adobe PDF | View/Open |
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