Coronary artery disease diagnosis using extra tree-support vector machine: ET-SVMRBF

2021 ◽  
Vol 66 (2) ◽  
pp. 209
Author(s):  
Rajneesh Kumar ◽  
Anurag Jain ◽  
Pooja Rani
Keyword(s):  
Coronary Artery Disease ◽  
Support Vector Machine ◽  
Coronary Artery ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Artery Disease ◽  
Coronary Artery Disease Diagnosis

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

2012 ◽  
Vol 3 (1) ◽  
pp. 59-79 ◽  
Author(s):  
Roohallah Alizadehsani ◽  
Mohammad Javad Hosseini ◽  
Reihane Boghrati ◽  
Asma Ghandeharioun ◽  
Fahime Khozeimeh ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
High Sensitivity ◽  
Disease Diagnosis ◽  
Major Type ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Diagnostic Modality ◽  
Data Mining Algorithms ◽  
Artery Disease

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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