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.

scholarly journals Machine Learning Algorithms for Predicting Coronary Artery Disease: Efforts Toward an Open Source Solution

2020 ◽  
Author(s):  
Aravind Akella ◽  
Vibhor Kaushik
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Network Model ◽  
Neural Network Model ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Artery Disease

AbstractThe development of Coronary Artery Disease (CAD), one of the most prevalent diseases in the world, is heavily influenced by several modifiable risk factors. Predictive models built using machine learning (ML) algorithms may assist healthcare practitioners in timely detection of CAD, and ultimately, may improve outcomes. In this study, we have applied six different ML algorithms to predict the presence of CAD amongst patients listed in an openly available dataset provided by the University of California Irvine (UCI) Machine Learning Repository, named “the Cleveland dataset.” 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 highest of the six models (0.93). Additionally, five of the six algorithms resulted in very similar AUC-ROC curves. The AUC-ROC curve corresponding to the “Neural Network” algorithm is slightly steeper implying higher “true positive percentage” achieved with this model. We also extracted the variables of importance in the “Neural Network” model to help in the risk assessment. We have released the full computer code generated in this study in the public domain as a preliminary effort toward developing an open solution for predicting the presence of coronary artery disease in a given population and present a workflow model for implementing a possible solution.

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 A Machine Learning Model Utilizing a Novel SNP Shows Enhanced Prediction of Coronary Artery Disease Severity

Genes ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1446
Author(s):  
Tanyaporn Pattarabanjird ◽  
Corban Cress ◽  
Anh Nguyen ◽  
Angela Taylor ◽  
Stefan Bekiranov ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Severity Score ◽  
Clinical Decision Making ◽  
Confusion Matrix ◽  
Operating Characteristics ◽  
Increased Risk ◽  
Artery Disease

Background: Machine learning (ML) has emerged as a powerful approach for predicting outcomes based on patterns and inferences. Improving prediction of severe coronary artery disease (CAD) has the potential for personalizing prevention and treatment strategies and for identifying individuals that may benefit from cardiac catheterization. We developed a novel ML approach combining traditional cardiac risk factors (CRF) with a single nucleotide polymorphism (SNP) in a gene associated with human CAD (ID3 rs11574) to enhance prediction of CAD severity; Methods: ML models incorporating CRF along with ID3 genotype at rs11574 were evaluated. The most predictive model, a deep neural network, was used to classify patients into high (>32) and low level (≤32) Gensini severity score. This model was trained on 325 and validated on 82 patients. Prediction performance of the model was summarized by a confusion matrix and area under the receiver operating characteristics curve (ROC-AUC); and Results: Our neural network predicted severity score with 81% and 87% accuracy for the low and the high groups respectively with an ROC-AUC of 0.84 for 82 patients in the test group. The addition of ID3 rs11574 to CRF significantly enhanced prediction accuracy from 65% to 81% in the low group, and 72% to 84% in the high group. Age, high-density lipoprotein (HDL), and systolic blood pressure were the top 3 contributors in predicting severity score; Conclusions: Our neural network including ID3 rs11574 improved prediction of CAD severity over use of Framingham score, which may potentially be helpful for clinical decision making in patients at increased risk of complications from coronary angiography.

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.

Detection of coronary artery disease in patients with chest pain: An machine learning model based on magnetocardiography parameters

2020 ◽  
pp. 1-10
Author(s):  
Huang Xiao ◽  
Chen Pengfei ◽  
Tang Fakuan ◽  
Hua Ning
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Chest Pain ◽  
Coronary Artery ◽  
Cross Sectional Study ◽  
Cross Sectional ◽  
Mlp Neural Network ◽  
Artery Disease ◽  
Sensitivity Specificity

BACKGROUD: Patients with chest pain and suspected of coronary artery disease(CAD) need further test to confirm the diagnosis. Magnetocardiography (MCG) is a non-invasive and emission-free technology which can detect and measure the weak magnetic fields created by the electrical activity of the heart. OBJECTIVE: This study aimed to investigate the usefulness of the 10 MCG parameters to detect CAD in patients with chest pain by means of a machine learning method of multilayer perceptron(MLP) neural network. METHODS: 209 patients who were suffering from chest pain and suspected of CAD were enrolled in this cross-sectional study. In all patients, 12-lead electrocardiography(ECG) and MCG test were performed before percutaneous coronary angiography(PCA). 10 MCG parameters were analyzed by MLP neural networks. RESULTS: 11 diagnostic models(M1 to M11) were established after MLP analysis. The accuracies ranged from 71.2% to 90.5%. Two models(M10 and M11) were further analyzed. The accuracy, sensitivity, specificity, PPV, NPV, PLR and NLR were 89.5%, 89.8%, 88.9%, 92.7%, 84.7%, 11.10 and 0.11, of M10, and were 90.0%, 91.4%, 87.7%, 92.1%, 86.6%, 7.43 and 0.10, of M11. CONCLUSIONS: By a method of MLP neural network, MCG is applicable in identifying CAD in patients with chest pain, which seems beneficial for detection of CAD.

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