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Parsimonious evolutionary-based model development for detecting artery disease
conference contribution
posted on 2019-01-01, 00:00 authored by Seyed Mohammad Jafar Jalali, Abbas KhosraviAbbas Khosravi, Roohallah Alizadehsani, Syed Salaken, P Kebria, R Puri, Saeid Nahavandi© 2019 IEEE. Coronary artery disease (CAD) is the most common cardiovascular condition. It often leads to a heart attack causing millions of deaths worldwide. Its accurate prediction using data mining techniques could reduce treatment risks and costs and save million lives. Motivated by these, this study proposes a framework for developing parsimonious models for CAD detection. A novel feature selection method called weight by Support Vector Machine is first applied to identify most informative features for model development. Then two evolutionary-based models called genetic programming expression (GEP) and genetic algorithm-emotional neural network (GA-ENN) are implemented for CAD prediction. Obtained results indicate that the GEP models outperform GA-ENN models and achieve the state of the art accuracy of 90%. Such a precise model could be used as an assistive tool for medical diagnosis as well as training purposes.
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Event
Industrial Technology. Conference (2019 : Melbourne, Victoria)Pagination
800 - 805Publisher
IEEELocation
Melbourne, Victoria)Place of publication
Piscataway, N.J.Publisher DOI
Start date
2019-02-13End date
2019-02-15ISBN-13
9781538663769Language
engPublication classification
E1 Full written paper - refereedTitle of proceedings
ICIT 2019 : Proceedings of the IEEE International Conference on Industrial TechnologyUsage metrics
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