scholarly journals A System for Diagnosis of Coronary Artery Disease based on Neural Networks and Machine Learning Algorithms

2018 ◽  
Vol 181 (15) ◽  
pp. 36-41
Author(s):  
Najmeh Samadiani ◽  
Zeinab Hassani
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Artery Disease

Accuracy detection of coronary artery disease using machine learning algorithms

2021 ◽  
Author(s):  
Harinder Singh ◽  
Tasneem Bano Rehman ◽  
Ch. Gangadhar ◽  
Rohit Anand ◽  
Nidhi Sindhwani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Artery Disease

scholarly journals Machine Learning and Statistical Approaches for Classification of Risk of Coronary Artery Disease using Plasma Cytokines.

2020 ◽  
Author(s):  
Seema Singh Saharan ◽  
Pankaj Nagar ◽  
Kate Townsend Creasy ◽  
Eveline O. Stock ◽  
James Feng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
At Risk ◽  
Coronary Artery ◽  
Random Forest ◽  
Large Scale ◽  
Statistical Significance ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Artery Disease

Abstract Background As per the 2017 WHO fact sheet, Coronary Artery Disease (CAD) is the primary cause of death in the world, and accounts for 31% of total fatalities. The unprecedented 17.6 million deaths caused by CAD in 2016 underscores the urgent need to facilitate proactive and accelerated pre-emptive diagnosis. The current research took an innovative approach to implement K Nearest Neighbor (k-NN) and ensemble Random Forest Machine Learning algorithms to achieve a targeted “At Risk” Coronary Artery Disease (CAD) classification. To ensure better generalizability mechanisms like k-fold cross validation, hyperparameter tuning and statistical significance (p<.05) were employed. The classification is also unique from the aspect of incorporating 35 cytokines as biomarkers within the predictive feature space of Machine Learning algorithms.Results A total of seven classifiers were developed, with four built using 35 cytokine predictive features and three built using 9 cytokines statistically significant (p<.05) across CAD versus Control groups determined by independent two sample t tests. The best prediction accuracy of 100% was achieved by Random Forest ensemble using nine significant cytokines. Significant cytokines were selected to decrease the noise level of the data, allowing for better classification. Additionally, from the bio-medical perspective, it was enlightening to empirically observe the interplay of the cytokines. Compared to Controls, moderately correlated (correlation coefficient r=.5) cytokines “IL1-β”, “IL-10” were both significant and down regulated in the CAD group. Both cytokines were primarily responsible for the Random forest generated 100% classification. In conjunction with Machine Learning (ML) algorithms, the traditional statistical techniques like correlation and t tests were leveraged to obtain insights that brought forth a role for cytokines in the investigation of CAD risk.Conclusions Presently, as large-scale efforts are gaining momentum to enable early detection of individuals at risk for CAD by the application of novel and powerful ML algorithms, detection can be further improved by incorporating additional biomarkers. Investigation of emerging role of cytokines in CAD can materially enhance the detection of risk and the discovery of mechanisms of disease that can lead to new therapeutic approaches.

scholarly journals Machine learning algorithms for predicting coronary artery disease: efforts toward an open source solution

2021 ◽  
pp. FSO698
Author(s):  
Aravind Akella ◽  
Sudheer Akella
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Open Source ◽  
Computer Code ◽  
Machine Learning Algorithms ◽  
Clinical Tool ◽  
The Neural Network ◽  
Artery Disease

Aim: The development of coronary artery disease (CAD), a highly prevalent disease worldwide, is influenced by several modifiable risk factors. Predictive models built using machine learning (ML) algorithms may assist clinicians in timely detection of CAD and may improve outcomes. Materials & methods: In this study, we applied six different ML algorithms to predict the presence of CAD amongst patients listed in ‘the Cleveland dataset.’ The generated computer code is provided as a working open source solution with the ultimate goal to achieve a viable clinical tool for CAD detection. Results: All six ML algorithms achieved accuracies greater than 80%, with the ‘neural network’ algorithm achieving accuracy greater than 93%. The recall achieved with the ‘neural network’ model is also the highest of the six models (0.93), indicating that predictive ML models may provide diagnostic value in CAD.

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.

scholarly journals Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 551
Author(s):  
Chris Boyd ◽  
Greg Brown ◽  
Timothy Kleinig ◽  
Joseph Dawson ◽  
Mark D. McDonnell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Model Comparison ◽  
Disease Risk ◽  
Imaging Modalities ◽  
Current Status ◽  
Advantages And Disadvantages ◽  
Body Regions ◽  
Artery Disease

Research into machine learning (ML) for clinical vascular analysis, such as those useful for stroke and coronary artery disease, varies greatly between imaging modalities and vascular regions. Limited accessibility to large diverse patient imaging datasets, as well as a lack of transparency in specific methods, are obstacles to further development. This paper reviews the current status of quantitative vascular ML, identifying advantages and disadvantages common to all imaging modalities. Literature from the past 8 years was systematically collected from MEDLINE® and Scopus database searches in January 2021. Papers satisfying all search criteria, including a minimum of 50 patients, were further analysed and extracted of relevant data, for a total of 47 publications. Current ML image segmentation, disease risk prediction, and pathology quantitation methods have shown sensitivities and specificities over 70%, compared to expert manual analysis or invasive quantitation. Despite this, inconsistencies in methodology and the reporting of results have prevented inter-model comparison, impeding the identification of approaches with the greatest potential. The clinical potential of this technology has been well demonstrated in Computed Tomography of coronary artery disease, but remains practically limited in other modalities and body regions, particularly due to a lack of routine invasive reference measurements and patient datasets.

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