Probabilistic Graphical Modeling for Estimating Risk of Coronary Artery Disease: Applications of a Flexible Machine-Learning Method

2019 ◽  
Vol 39 (8) ◽  
pp. 1032-1044 ◽  
Author(s):  
Alind Gupta ◽  
Justin J. Slater ◽  
Devon Boyne ◽  
Nicholas Mitsakakis ◽  
Audrey Béliveau ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Risk Prediction ◽  
Prediction Models ◽  
The United States ◽  
Individual Risk ◽  
Support Vector ◽  
Data Set ◽  
Artery Disease

Objectives. Coronary artery disease (CAD) is the leading cause of death and disease burden worldwide, causing 1 in 7 deaths in the United States alone. Risk prediction models that can learn the complex causal relationships that give rise to CAD from data, instead of merely predicting the risk of disease, have the potential to improve transparency and efficacy of personalized CAD diagnosis and therapy selection for physicians, patients, and other decision makers. Methods. We use Bayesian networks (BNs) to model the risk of CAD using the Z-Alizadehsani data set—a published real-world observational data set of 303 Iranian patients at risk for CAD. We also describe how BNs can be used for incorporation of background knowledge, individual risk prediction, handling missing observations, and adaptive decision making under uncertainty. Results. BNs performed on par with machine-learning classifiers at predicting CAD and showed better probability calibration. They achieved a mean 10-fold area under the receiver-operating characteristic curve (AUC) of 0.93 ± 0.04, which was comparable with the performance of logistic regression with L1 or L2 regularization (AUC: 0.92 ± 0.06), support vector machine (AUC: 0.92 ± 0.06), and artificial neural network (AUC: 0.91 ± 0.05). We describe the use of BNs to predict with missing data and to adaptively calculate prognostic values of individual variables under uncertainty. Conclusion. BNs are powerful and versatile tools for risk prediction and health outcomes research that can complement traditional statistical techniques and are particularly useful in domains in which information is uncertain or incomplete and in which interpretability is important, such as medicine.

scholarly journals Coronary Artery Disease Detection by Machine Learning with Coronary Bifurcation Features

2020 ◽  
Vol 10 (21) ◽  
pp. 7656
Author(s):  
Xueping Chen ◽  
Yi Fu ◽  
Jiangguo Lin ◽  
Yanru Ji ◽  
Ying Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Expansion Ratio ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Coronary Bifurcation ◽  
Linear Discriminant ◽  
Accurate Detection ◽  
Artery Disease

Background: Early accurate detection of coronary artery disease (CAD) is one of the most important medical research areas. Researchers are motivated to utilize machine learning techniques for quick and accurate detection of CAD. Methods: To obtain the high quality of features used for machine learning, we here extracted the coronary bifurcation features from the coronary computed tomography angiography (CCTA) images by using the morphometric method. The machine learning classifier algorithms, such as logistic regression (LR), decision tree (DT), linear discriminant analysis (LDA), k-nearest neighbors (k-NN), artificial neural network (ANN), and support vector machine (SVM) were applied for estimating the performance by using the measured features. Results: The results showed that in comparison with other machine learning methods, the polynomial-SVM with the use of the grid search optimization method had the best performance for the detection of CAD and had yielded the classification accuracy of 100.00%. Among six examined coronary bifurcation features, the exponent of vessel diameter (n) and the area expansion ratio (AER) were two key features in the detection of CAD. Conclusions: This study could aid the clinicians to detect CAD accurately, which may probably provide an alternative method for the non-invasive diagnosis in clinical.

scholarly journals Machine learning as a tool for diagnostic and prognostic research in coronary artery disease

2020 ◽  
Vol 25 (12) ◽  
pp. 3999
Author(s):  
B. I. Geltser ◽  
M. M. Tsivanyuk ◽  
K. I. Shakhgeldyan ◽  
V. Yu. Rublev
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Large Data ◽  
Medical Decision ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Advantages And Disadvantages ◽  
Predictive Algorithms ◽  
Artery Disease

Machine learning (ML) are the central tool of artificial intelligence, the use of which makes it possible to automate the processing and analysis of large data, reveal hidden or non-obvious patterns and learn a new knowledge. The review presents an analysis of literature on the use of ML for diagnosing and predicting the clinical course of coronary artery disease. We provided information on reference databases, the use of which allows to develop models and validate them (European ST-T Database, Cleveland Heart Disease database, Multi-Ethnic Study of Atherosclerosis, etc.). The advantages and disadvantages of individual ML methods (logistic regression, support vector machines, decision trees, naive Bayesian classifier, k-nearest neighbors) for the development of diagnostic and predictive algorithms are shown. The most promising ML methods include deep learning, which is implemented using multilayer artificial neural networks. It is assumed that the improvement of ML-based models and their introduction into clinical practice will help support medical decision-making, increase the effectiveness of treatment and optimize health care costs.

scholarly journals Machine learning prediction in cardiovascular diseases: a meta-analysis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Chayakrit Krittanawong ◽  
Hafeez Ul Hassan Virk ◽  
Sripal Bangalore ◽  
Zhen Wang ◽  
Kipp W. Johnson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Cardiovascular Diseases ◽  
Cardiac Arrhythmias ◽  
Predictive Ability ◽  
Support Vector ◽  
Boosting Algorithms ◽  
Artery Disease

Abstract Several machine learning (ML) algorithms have been increasingly utilized for cardiovascular disease prediction. We aim to assess and summarize the overall predictive ability of ML algorithms in cardiovascular diseases. A comprehensive search strategy was designed and executed within the MEDLINE, Embase, and Scopus databases from database inception through March 15, 2019. The primary outcome was a composite of the predictive ability of ML algorithms of coronary artery disease, heart failure, stroke, and cardiac arrhythmias. Of 344 total studies identified, 103 cohorts, with a total of 3,377,318 individuals, met our inclusion criteria. For the prediction of coronary artery disease, boosting algorithms had a pooled area under the curve (AUC) of 0.88 (95% CI 0.84–0.91), and custom-built algorithms had a pooled AUC of 0.93 (95% CI 0.85–0.97). For the prediction of stroke, support vector machine (SVM) algorithms had a pooled AUC of 0.92 (95% CI 0.81–0.97), boosting algorithms had a pooled AUC of 0.91 (95% CI 0.81–0.96), and convolutional neural network (CNN) algorithms had a pooled AUC of 0.90 (95% CI 0.83–0.95). Although inadequate studies for each algorithm for meta-analytic methodology for both heart failure and cardiac arrhythmias because the confidence intervals overlap between different methods, showing no difference, SVM may outperform other algorithms in these areas. The predictive ability of ML algorithms in cardiovascular diseases is promising, particularly SVM and boosting algorithms. However, there is heterogeneity among ML algorithms in terms of multiple parameters. This information may assist clinicians in how to interpret data and implement optimal algorithms for their dataset.

scholarly journals Coronary Artery Disease Diagnosis: Ranking the Significant Features Using Random Trees Model

2020 ◽  
Author(s):  
Javad Hassannataj Joloudari ◽  
Edris Hassannataj Joloudari ◽  
Hamid Saadatfar ◽  
Mohammad Ghasemigol ◽  
Seyyed Mohammad Razavi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Heart Disease ◽  
Coronary Artery ◽  
Decision Tree ◽  
Disease Diagnosis ◽  
Random Trees ◽  
Support Vector ◽  
Interaction Detection ◽  
Artery Disease

Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis by selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), the decision tree of C5.0, support vector machine (SVM), the decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models.

scholarly journals Utility of family history in disease prediction in the era of polygenic scores

2021 ◽  
Author(s):  
Brooke N Wolford ◽  
Ida Surakka ◽  
Sarah E Graham ◽  
Jonas B Nielsen ◽  
Wei Zhou ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Family History ◽  
Risk Prediction ◽  
Prediction Models ◽  
Preventive Treatment ◽  
Health Study ◽  
Polygenic Scores ◽  
Artery Disease ◽  
The Uk

Clinicians have historically used family history and other risk prediction algorithms to guide patient care and preventive treatment such as statin therapeutics for coronary artery disease. As polygenic scores move towards clinical use, we have begun to consider the interplay of these scores with other predictors for optimal second generation risk prediction. Here, we assess the use of family history and polygenic scores as independent predictors of coronary artery disease and type 2 diabetes. We highlight considerations for use of family history as a predictor of these two diseases after evaluating their effectiveness in the Trøndelag Health Study and the UK Biobank. From these, we advocate for collection of high resolution family history variables in biobanks for future prediction models.

scholarly journals Population Bias in Polygenic Risk Prediction Models for Coronary Artery Disease

2020 ◽  
Vol 13 (6) ◽  
Author(s):  
Damian Gola ◽  
Jeanette Erdmann ◽  
Kristi Läll ◽  
Reedik Mägi ◽  
Bertram Müller-Myhsok ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Risk Prediction ◽  
Prediction Models ◽  
Data Sets ◽  
Risk Prediction Models ◽  
Polygenic Risk ◽  
Testing Data ◽  
Artery Disease ◽  
European Populations

Background: Individual risk prediction based on genome-wide polygenic risk scores (PRSs) using millions of genetic variants has attracted much attention. It is under debate whether PRS models can be applied—without loss of precision—to populations of similar ethnic but different geographic background than the one the scores were trained on. Here, we examine how PRS trained in population-specific but European data sets perform in other European subpopulations in distinguishing between coronary artery disease patients and healthy individuals. Methods: We use data from UK and Estonian biobanks (UKB, EB) as well as case-control data from the German population (DE) to develop and evaluate PRS in the same and different populations. Results: PRSs have the highest performance in their corresponding population testing data sets, whereas their performance significantly drops if applied to testing data sets from different European populations. Models trained on DE data revealed area under the curves in independent testing sets in DE: 0.6752, EB: 0.6156, and UKB: 0.5989; trained on EB and tested on EB: 0.6565, DE: 0.5407, and UKB: 0.6043; trained on UKB and tested on UKB: 0.6133, DE: 0.5143, and EB: 0.6049. Conclusions: This result has a direct impact on the clinical usability of PRS for risk prediction models using PRS: a population effect must be kept in mind when applying risk estimation models, which are based on additional genetic information even for individuals from different European populations of the same ethnicity.

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 Coronary Artery Disease Diagnosis; Ranking the Significant Features Using a Random Trees Model

2020 ◽  
Vol 17 (3) ◽  
pp. 731 ◽  
Author(s):  
Javad Hassannataj Joloudari ◽  
Edris Hassannataj Joloudari ◽  
Hamid Saadatfar ◽  
Mohammad Ghasemigol ◽  
Seyyed Mohammad Razavi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Heart Disease ◽  
Coronary Artery ◽  
Decision Tree ◽  
Disease Diagnosis ◽  
Random Trees ◽  
Support Vector ◽  
Interaction Detection ◽  
Artery Disease

Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), and decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models.

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