scholarly journals Prediction of the fact and degree of coronary artery disease using the processing of clinical and instrumental data by artificial intelligence

2021 ◽  
Vol 16 (3) ◽  
pp. 153-158
Author(s):  
Timur Abdualimov ◽  
◽  
Andrey Obrezan ◽  
◽  
Keyword(s):  
Neural Network ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Holter Monitoring ◽  
Training Sample ◽  
Diagnostic Methods ◽  
Treadmill Test ◽  
Transient Myocardial Ischemia ◽  
Artery Disease ◽  
Trained Neural Network

Aim of the study was to analyze the possibility of using neural network analysis to predict the severity of coronary bed lesion. The study was also designated to determine the performance and accuracy of the trained neural network model receiving input as the structured data and EGG images with the parameters and leads positioning differ from the training sample, and also to compare the efficiency of detecting transient myocardial ischemia with traditional diagnostic methods, such as 24-hour Holter monitoring, treadmill test. Neural network analysis of the available clinical, laboratory and instrumentation data allow to configure the network parameters for further prediction of coronary artery disease. The results obtained in the form of an AUC score allow to consider this method to be effective in the coronary artery disease diagnosis using recorded ECG tape with parameters and lead positioning differ from initial training sample. The efficiency of transient myocardial ischemia detection on the training sample of the trained neural network is higher in comparison with traditional diagnostic methods, such as 24-hour Holter monitoring, treadmill test.

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.

Identification of Coronary Artery Disease using Artificial Neural Network and Case Based Reasoning

2020 ◽  
Vol 13 ◽  
Author(s):  
Varun Sapra ◽  
M.L Saini ◽  
Luxmi Verma
Keyword(s):  
Neural Network ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Early Stage ◽  
Second Phase ◽  
Case Based Reasoning ◽  
Data Set ◽  
Artery Disease ◽  
Severity Of The Disease ◽  
Case Based

Background: Cardiovascular diseases are increasing at an alarming rate with very high rate of mortality. Coronary artery disease is one of the type of cardiovascular disease, which is not easily diagnosed in its early stage. Prevention of Coronary Artery Disease is possible only if it is diagnosed, at early stage and proper medication is done. Objective: An effective diagnosis model is important not only for the early diagnosis but also to check the severity of the disease. Method: In this paper, a hybrid approach is followed, with the integration of deep learning (multi-layer perceptron) with Case based reasoning to design analytical framework. This paper suggests two phases of the study, one in which the patient is diagnosed for Coronary artery disease and in second phase, if the patient is suffering from the disease then employing Case based reasoning to diagnose the severity of the disease. In the first phase, multilayer perceptron is implemented on reduced dataset and with time-based learning for stochastic gradient descent respectively. Results: The classification accuracy is increase by 4.18 % with reduced data set using deep neural network with time based learning. In second phase, if the patient is diagnosed as positive for Coronary artery disease, then it triggers the Case based reasoning system to retrieve from the case base, the most similar case to predict the severity for that patient. The CBR model achieved 97.3% accuracy. Conclusion: The model can be very useful for medical practitioners as a supporting decision system and thus can save the patients from unnecessary medical expenses on costly tests and can improve the quality and effectiveness of medical treatment.

The study of 24-hour holter monitoring in patients with coronary artery disease

1993 ◽  
Vol 23 (2) ◽  
pp. 184
Author(s):  
Hyun Jin Park ◽  
Eun Kyung Cho ◽  
Yoon Bo Yoon ◽  
Yong Jun Kim ◽  
Sang Min Lee ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Holter Monitoring ◽  
Artery Disease

Activity of transient myocardial ischemia out of hospital in coronary artery disease and implications for management

1985 ◽  
Vol 56 (17) ◽  
pp. I19-I22 ◽  
Author(s):  
Michael B. Rocco ◽  
Stephen Campbell ◽  
Joan Barry ◽  
George Rebecca ◽  
Elizabeth Nabel ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Myocardial Ischemia ◽  
Transient Myocardial Ischemia ◽  
Artery Disease

scholarly journals Diagnostic Performance of CMR, SPECT, and PET Imaging for the Identification of Coronary Artery Disease: A Meta-Analysis

2021 ◽  
Vol 8 ◽  
Author(s):  
Jianfeng Xu ◽  
Fei Cai ◽  
Changran Geng ◽  
Zheng Wang ◽  
Xiaobin Tang
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Likelihood Ratio ◽  
Subgroup Analysis ◽  
Diagnostic Performance ◽  
Meta Analysis ◽  
Diagnostic Methods ◽  
Cochrane Library ◽  
Emission Computed Tomography ◽  
Artery Disease

Background: Myocardial perfusion imaging modalities, such as cardiac magnetic resonance (CMR), single-photon emission computed tomography (SPECT), and positron emission tomography (PET), are well-established non-invasive diagnostic methods to detect hemodynamically significant coronary artery disease (CAD). The aim of this meta-analysis is to compare CMR, SPECT, and PET in the diagnosis of CAD and to provide evidence for further research and clinical decision-making.Methods: PubMed, Web of Science, EMBASE, and Cochrane Library were searched. Studies that used CMR, SPECT, and/or PET for the diagnosis of CAD were included. Pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio with their respective 95% confidence interval, and the area under the summary receiver operating characteristic (SROC) curve were calculated.Results: A total of 203 articles were identified for inclusion in this meta-analysis. The pooled sensitivity values of CMR, SPECT, and PET were 0.86, 0.83, and 0.85, respectively. Their respective overall specificity values were 0.83, 0.77, and 0.86. Results in subgroup analysis of the performance of SPECT with 201Tl showed the highest pooled sensitivity [0.85 (0.82, 0.88)] and specificity [0.80 (0.75, 0.83)]. 99mTc-tetrofosmin had the lowest sensitivity [0.76 (0.67, 0.82)]. In the subgroup analysis of PET tracers, results indicated that 13N had the lowest pooled sensitivity [0.83 (0.74, 0.89)], and the specificity was the highest [0.91 (0.81, 0.96)].Conclusion: Our meta-analysis indicates that CMR and PET present better diagnostic performance for the detection of CAD as compared with SPECT.

Sign in / Sign up

Export Citation Format

Share Document