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Exerting Cost-Sensitive and Feature Creation Algorithms for Coronary Artery Disease Diagnosis

Exerting Cost-Sensitive and Feature Creation Algorithms for Coronary Artery Disease Diagnosis

Roohallah Alizadehsani, Mohammad Javad Hosseini, Reihane Boghrati, Asma Ghandeharioun, Fahime Khozeimeh, Zahra Alizadeh Sani
Copyright: © 2012 |Volume: 3 |Issue: 1 |Pages: 21
ISSN: 1947-9115|EISSN: 1947-9123|EISBN13: 9781466613218|DOI: 10.4018/jkdb.2012010104
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MLA

Alizadehsani, Roohallah, et al. "Exerting Cost-Sensitive and Feature Creation Algorithms for Coronary Artery Disease Diagnosis." IJKDB vol.3, no.1 2012: pp.59-79. http://doi.org/10.4018/jkdb.2012010104

APA

Alizadehsani, R., Hosseini, M. J., Boghrati, R., Ghandeharioun, A., Khozeimeh, F., & Sani, Z. A. (2012). Exerting Cost-Sensitive and Feature Creation Algorithms for Coronary Artery Disease Diagnosis. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 3(1), 59-79. http://doi.org/10.4018/jkdb.2012010104

Chicago

Alizadehsani, Roohallah, et al. "Exerting Cost-Sensitive and Feature Creation Algorithms for Coronary Artery Disease Diagnosis," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 3, no.1: 59-79. http://doi.org/10.4018/jkdb.2012010104

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Abstract

One of the main causes of death the world over is the family of cardiovascular diseases, of which coronary artery disease (CAD) is a major type. Angiography is the principal diagnostic modality for the stenosis of heart arteries; however, it leads to high complications and costs. The present study conducted data-mining algorithms on the Z-Alizadeh Sani dataset, so as to investigate rule based and feature based classifiers and their comparison, and the reason for the effectiveness of a preprocessing algorithm on a dataset. Misclassification of diseased patients has more side effects than that of healthy ones. To this end, this paper employs 10-fold cross-validation on cost-sensitive algorithms along with base classifiers of Naïve Bayes, Sequential Minimal Optimization (SMO), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and C4.5 and the results show that the SMO algorithm yielded very high sensitivity (97.22%) and accuracy (92.09%) rates.

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