DocumentCode
330887
Title
Internet-based system for diagnosis of coronary artery disease
Author
Lovelace, J.J. ; Cios, K.J. ; Sala, D.M. ; Goodenday, L.S.
Author_Institution
Toledo Univ., OH, USA
fYear
1998
fDate
13-16 Sep 1998
Firstpage
45
Lastpage
48
Abstract
An intelligent Internet-based coronary artery disease (CAD) diagnostic system based on Tl-201 planar imaging data was developed. The system can self-generate rules to correctly diagnose obstructions in the three main arteries, namely right coronary artery (RCA), left anterior descending (LAD), circumflex (CCX), or a patient being normal (with no obstructed arteries). The rule formation engine is based on the CLIP3 (Cover Learning using Integer Programming version 3) machine learning algorithm. The system stores all data (training data, fuzzy sets and rules) into a relational database. The primary consideration of the authors´ design was to keep the system easy to use, thus the database operations are transparent to the user. The architecture of the system is a multithreaded structure that allows multiple operations to be carried out in parallel. The biggest advantage of the system is that a diagnosis can be carried out over the Internet via HTML forms
Keywords
Internet; blood vessels; cardiology; diseases; medical expert systems; medical image processing; radioisotope imaging; relational databases; CLIP3 machine learning algorithm; HTML forms; Tl; Tl-201 planar imaging data; circumflex artery; diagnostic nuclear medicine; fuzzy sets; left anterior descending artery; medical diagnostic imaging; multithreaded structure; obstructed arteries; right coronary artery; rule formation engine; training data; Arteries; Coronary arteriosclerosis; Fuzzy sets; Internet; Linear programming; Machine learning; Machine learning algorithms; Relational databases; Search engines; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Computers in Cardiology 1998
Conference_Location
Cleveland, OH
ISSN
0276-6547
Print_ISBN
0-7803-5200-9
Type
conf
DOI
10.1109/CIC.1998.731705
Filename
731705
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