Intra-Vascular Ultra Sound Image Classification System for the Diagnosis of Coronary Artery Disease Using Adaptive Wavelet Transform with Support Vector Machine

2021 ◽  
Vol 11 (5) ◽  
pp. 1509-1516
Author(s):  
A. Swarnalatha ◽  
M. Manikandan
Keyword(s):  
Coronary Artery Disease ◽  
Support Vector Machine ◽  
Wavelet Transform ◽  
Coronary Artery ◽  
Classification Systems ◽  
Ultra Sound ◽  
Support Vector ◽  
Decomposition Approach ◽  
Empirical Wavelet Transform ◽  
Artery Disease

In this study, an efficient Decision Support System (DSS) is presented to classify coronary artery disease using Intra-Vascular Ultra Sound (IVUS) images. IVUS images are commonly used to diagnose coronary artery diseases. Wavelet transform is a multiresolution texture analysis tool which is applied to various image analysis and classification systems. Unlike the wavelet transform, Empirical Wavelet Transform (EWT) is a dependent decomposition approach that provides superior temporal and frequency information. Hence, EWT is considered as a feature extraction approach in this study. Before extracting EWT features, an adaptive non-linear speckle reducing filter; Lee filter is used to remove the IVUS images’ noises. The accumulated energies of EWT sub-bands are computed and fed to four Support Vector Machine (SVM) for coronary plague classification into five different classes; normal, calcium, fibrous, necrotic (thrombus) and soft plague (fibro-fatty). A total number of 400 IVUS images and their corresponding labeling are obtained from Shifa hospitals, Tirunelveli, Tamilnadu, India. Results prove that the classification of coronary plague is done with higher accuracy by using the EWT-SVM approach.

scholarly journals Using Anti-Malondialdehyde Modified Peptide Autoantibodies to Import Machine Learning for Predicting Coronary Artery Stenosis in Taiwanese Patients with Coronary Artery Disease

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 961
Author(s):  
Yu-Cheng Hsu ◽  
I-Jung Tsai ◽  
Hung Hsu ◽  
Po-Wen Hsu ◽  
Ming-Hui Cheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Support Vector Machine ◽  
Coronary Artery ◽  
Random Forest ◽  
Decision Tree ◽  
Protein Adducts ◽  
Support Vector ◽  
Modified Peptides ◽  
Artery Disease

Machine learning (ML) algorithms have been applied to predicting coronary artery disease (CAD). Our purpose was to utilize autoantibody isotypes against four different unmodified and malondialdehyde (MDA)-modified peptides among Taiwanese with CAD and healthy controls (HCs) for CAD prediction. In this study, levels of MDA, MDA-modified protein (MDA-protein) adducts, and autoantibody isotypes against unmodified peptides and MDA-modified peptides were measured with enzyme-linked immunosorbent assay (ELISA). To improve the performance of ML, we used decision tree (DT), random forest (RF), and support vector machine (SVM) coupled with five-fold cross validation and parameters optimization. Levels of plasma MDA and MDA-protein adducts were higher in CAD patients than in HCs. IgM anti-IGKC76–99 MDA and IgM anti-A1AT284–298 MDA decreased the most in patients with CAD compared to HCs. In the experimental results of CAD prediction, the decision tree classifier achieved an area under the curve (AUC) of 0.81; the random forest classifier achieved an AUC of 0.94; the support vector machine achieved an AUC of 0.65 for differentiating between CAD patients with stenosis rates of 70% and HCs. In this study, we demonstrated that autoantibody isotypes imported into machine learning algorithms can lead to accurate models for clinical use.

scholarly journals Automated Coronary Artery Disease Detection using RQA Features and Quadratic Support Vector Machine

2020 ◽  
Vol 8 (5) ◽  
pp. 2799-2804
Keyword(s):  
Coronary Artery Disease ◽  
Support Vector Machine ◽  
Coronary Artery ◽  
Cardiac Activity ◽  
Normal Subjects ◽  
Support Vector ◽  
Svm Classifier ◽  
Normal Heart ◽  
Artery Disease ◽  
Heart Condition

One of the major causes of death globally due to heart disease is the coronary artery disease (CAD). Due to CAD the blood flow to the cardiac muscle is reduced. The progression of this process eventually causes Myocardial Infarction (MI) that result in sudden death. Hence the detection of CAD at early phase is essential. The electrocardiogram (ECG) is mainly used to capture the abnormal cardiac activity for CAD. But the difficulties in manual interpretation of ECG signal leads to error in CAD detection. To overcome the difficulty in CAD diagnostic task, we have proposed a computer aided methodology using the heart rate variability signal (HRV) for auto diagnosis of CAD and Normal heart condition. The hidden characteristics of HRV signal are identified through Recurrence Plot (RP) and the hidden information is quantified by Recurrence quantification analysis (RQA). The extracted RQA based nonlinear features are analyzed for their clinical significance. The set the effective features are used for classification and subjected to three types of Support vector machine (SVM) classifier to discriminate CAD and the normal heart condition. The ECG database of CAD and normal subjects are taken from Physio.net database to obtain the experimental results. The highest diagnostic ability of the classifier is obtained by quadratic SVM with the accuracy of 98.83% where as the linear and cubic SVM classifier provide 97.22 % and 98.37 % classification accuracy respectively.

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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