Hybrid genetic‐discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries

Author:

Alizadehsani Roohallah1ORCID,Roshanzamir Mohamad2,Abdar Moloud1,Beykikhoshk Adham3,Khosravi Abbas1,Nahavandi Saeid1,Plawiak Pawel45,Tan Ru San6,Acharya U Rajendra789

Affiliation:

1. Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Geelong Victoria Australia

2. Department of Engineering, Fasa Branch Islamic Azad University Fasa Fars Iran

3. Centre for Pattern Recognition and Data Analytics Deakin University Geelong Victoria Australia

4. Department of Information and Communications Technology, Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 st., F‐3, 31‐155 Krakow Poland

5. Institute of Theoretical and Applied Informatics Polish Academy of Sciences Bałtycka 5, 44‐100 Gliwice Poland

6. National Heart Centre Singapore Singapore

7. Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore

8. Department of Biomedical Engineering, School of Science and Technology Singapore University of Social Sciences Singapore

9. Department of Bioinformatics and Medical Engineering Asia University Taiwan

Publisher

Wiley

Subject

Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Control and Systems Engineering

Reference53 articles.

1. Using decision trees in data mining for predicting factors influencing of heart disease;Abdar M.;Carpathian Journal of Electronic and Computer Engineering,2015

2. Comparing Performance of Data Mining Algorithms in Prediction Heart Diseases

3. Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study

4. Aimin Z. Yaochu J. Qingfu Z. Sendhoff B. &Tsang E.(2006 July).Combining model‐based and genetics‐based offspring generation for multi‐objective optimization using a convergence criterion. Paper presented at the 2006 IEEE International Conference on Evolutionary Computation Vancouver Canada.

5. Machine learning-based coronary artery disease diagnosis: A comprehensive review

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