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

Authors:
Roohallah Alizadehsani;Mohammad Javad Hosseini;Reihane Boghrati;Asma Ghandeharioun;Fahime Khozeimeh;Zahra Alizadeh Sani
Affiliations:
Department of Computer Engineering, Sharif University of Technology, Tehran, Iran;Department of Computer Engineering, Sharif University of Technology, Tehran, Iran;Department of Computer Engineering, Sharif University of Technology, Tehran, Iran;Department of Computer Engineering, Sharif University of Technology, Tehran, Iran;Mashhad University of Medical Science, Mashhad, Iran;Tehran University of Medical Science, Tehran, Iran
Venue:
International Journal of Knowledge Discovery in Bioinformatics
Year:
2012

Citing 12
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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.