Automated diagnosis of Coronary Artery Disease using pattern recognition approach

2017 ◽  
Author(s):  
Usha Desai ◽  
C. Gurudas Nayak ◽  
G. Seshikala ◽  
Roshan J. Martis
Keyword(s):  
Coronary Artery Disease ◽  
Pattern Recognition ◽  
Coronary Artery ◽  
Automated Diagnosis ◽  
Pattern Recognition Approach ◽  
Artery Disease

Artificial neural network models for coronary artery disease

2020 ◽  
Vol 15 ◽  
Author(s):  
Elham Shamsara ◽  
Sara Saffar Soflaei ◽  
Mohammad Tajfard ◽  
Ivan Yamshchikov ◽  
Habibollah Esmaili ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Diagnostic Model ◽  
Early Prediction ◽  
Artificial Neural ◽  
Artery Disease

Background: Coronary artery disease (CAD) is an important cause of mortality and morbidity globally. Objective : The early prediction of the CAD would be valuable in identifying individuals at risk, and in focusing resources on its prevention. In this paper, we aimed to establish a diagnostic model to predict CAD by using three approaches of ANN (pattern recognition-ANN, LVQ-ANN, and competitive ANN). Methods: One promising method for early prediction of disease based on risk factors is machine learning. Among different machine learning algorithms, the artificial neural network (ANN) algo-rithms have been applied widely in medicine and a variety of real-world classifications. ANN is a non-linear computational model, that is inspired by the human brain to analyze and process complex datasets. Results: Different methods of ANN that are investigated in this paper indicates in both pattern recognition ANN and LVQ-ANN methods, the predictions of Angiography+ class have high accuracy. Moreover, in CNN the correlations between the individuals in cluster ”c” with the class of Angiography+ is strongly high. This accuracy indicates the significant difference among some of the input features in Angiography+ class and the other two output classes. A comparison among the chosen weights in these three methods in separating control class and Angiography+ shows that hs-CRP, FSG, and WBC are the most substantial excitatory weights in recognizing the Angiography+ individuals although, HDL-C and MCH are determined as inhibitory weights. Furthermore, the effect of decomposition of a multi-class problem to a set of binary classes and random sampling on the accuracy of the diagnostic model is investigated. Conclusion : This study confirms that pattern recognition-ANN had the most accuracy of performance among different methods of ANN. That’s due to the back-propagation procedure of the process in which the network classify input variables based on labeled classes. The results of binarization show that decomposition of the multi-class set to binary sets could achieve higher accuracy.

Automated Diagnosis of Coronary Artery Disease Based on Data Mining and Fuzzy Modeling

2008 ◽  
Vol 12 (4) ◽  
pp. 447-458 ◽  
Author(s):  
M.G. Tsipouras ◽  
T.P. Exarchos ◽  
D.I. Fotiadis ◽  
A.P. Kotsia ◽  
K.V. Vakalis ◽  
...  
Keyword(s):  
Data Mining ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Fuzzy Modeling ◽  
Automated Diagnosis ◽  
Artery Disease

Automated diagnosis of Coronary Artery Disease affected patients using LDA, PCA, ICA and Discrete Wavelet Transform

2013 ◽  
Vol 37 ◽  
pp. 274-282 ◽  
Author(s):  
Donna Giri ◽  
U. Rajendra Acharya ◽  
Roshan Joy Martis ◽  
S. Vinitha Sree ◽  
Teik-Cheng Lim ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Wavelet Transform ◽  
Coronary Artery ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Automated Diagnosis ◽  
Artery Disease

scholarly journals Enhanced Automated Diagnosis of Coronary Artery Disease Using Features Extracted From QT Interval Time Series and ST–T Waveform

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 129510-129524
Author(s):  
Lianke Yao ◽  
Changchun Liu ◽  
Peng Li ◽  
Jikuo Wang ◽  
Yuanyuan Liu ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Time Series ◽  
Coronary Artery ◽  
Qt Interval ◽  
Automated Diagnosis ◽  
Interval Time ◽  
Interval Time Series ◽  
Artery Disease

Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM

2017 ◽  
Vol 138 ◽  
pp. 117-126 ◽  
Author(s):  
Azam Davari Dolatabadi ◽  
Siamak Esmael Zadeh Khadem ◽  
Babak Mohammadzadeh Asl
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Automated Diagnosis ◽  
Artery Disease

Elevated level of circulatory sTLT1 induces inflammation through SYK/MEK/ERK signalling in coronary artery disease

2019 ◽  
Vol 133 (22) ◽  
pp. 2283-2299
Author(s):  
Apabrita Ayan Das ◽  
Devasmita Chakravarty ◽  
Debmalya Bhunia ◽  
Surajit Ghosh ◽  
Prakash C. Mandal ◽  
...  
Keyword(s):  
Risk Factors ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Chronic Inflammation ◽  
Ex Vivo ◽  
Human Subjects ◽  
Critical Role ◽  
Atherosclerotic Cardiovascular Disease ◽  
Tnf Α ◽  
Artery Disease

Abstract The role of inflammation in all phases of atherosclerotic process is well established and soluble TREM-like transcript 1 (sTLT1) is reported to be associated with chronic inflammation. Yet, no information is available about the involvement of sTLT1 in atherosclerotic cardiovascular disease. Present study was undertaken to determine the pathophysiological significance of sTLT1 in atherosclerosis by employing an observational study on human subjects (n=117) followed by experiments in human macrophages and atherosclerotic apolipoprotein E (apoE)−/− mice. Plasma level of sTLT1 was found to be significantly (P<0.05) higher in clinical (2342 ± 184 pg/ml) and subclinical cases (1773 ± 118 pg/ml) than healthy controls (461 ± 57 pg/ml). Moreover, statistical analyses further indicated that sTLT1 was not only associated with common risk factors for Coronary Artery Disease (CAD) in both clinical and subclinical groups but also strongly correlated with disease severity. Ex vivo studies on macrophages showed that sTLT1 interacts with Fcɣ receptor I (FcɣRI) to activate spleen tyrosine kinase (SYK)-mediated downstream MAP kinase signalling cascade to activate nuclear factor-κ B (NF-kB). Activation of NF-kB induces secretion of tumour necrosis factor-α (TNF-α) from macrophage cells that plays pivotal role in governing the persistence of chronic inflammation. Atherosclerotic apoE−/− mice also showed high levels of sTLT1 and TNF-α in nearly occluded aortic stage indicating the contribution of sTLT1 in inflammation. Our results clearly demonstrate that sTLT1 is clinically related to the risk factors of CAD. We also showed that binding of sTLT1 with macrophage membrane receptor, FcɣR1 initiates inflammatory signals in macrophages suggesting its critical role in thrombus development and atherosclerosis.

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