Machine learning prediction in cardiovascular diseases: a meta-analysis

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.

Predicting Australian Adults at High Risk of Cardiovascular Disease Mortality Using Standard Risk Factors and Machine Learning

“…The Framingham model assigns a person to the low-risk group if the score is < 20 and to the high-risk group if the score is ≥ 20. As the Framingham equation was designed to estimate 10-year CVD risk and in this study the follow up data is for 15 years, we have linearly transformed the 10-year risk of the Framingham model into 15-year risk [ 13 ]. Thus, the Framingham score risk threshold became 30 instead of 20.…”

“…In the US, Ambale-Venkatesh et al [ 11 ] and Kakadiaris et al [ 12 ] also used random forest and support vector machine, respectively, to predict CVD events and mortality in US populations. A 2020 meta-analysis assessing the predictive ability of machine learning algorithms for cardiovascular diseases found promising potential in ML approaches [ 13 ]. The Framingham Risk Score is recommended for use in Australia to predict CVD risk but has been found to have limited accuracy for some Australian sub-populations [ 7 , 14 ].…”

Imaging Cardiovascular Inflammation in the COVID-19 Era

“…Artificial intelligence (AI) and machine learning algorithms have been already used in cardiovascular medicine for imaging based cardiovascular risk assessment [ 108 , 109 , 110 ]. The rapidly accumulating and broadly various data acquired from COVID-19 studies overburden medical professionals in finding the most efficient diagnostic and therapeutic methods.…”

Optimal Classification of Atrial Fibrillation and Congestive Heart Failure Using Machine Learning

“…The classification performance of the k-NN algorithm depends on the features used as the input of the k-NN algorithm and the k-value of the k-NN algorithm. To ensure the best possible optimization, the optimal parameters were selected, including the feature selection of features-activity, mobility, complexity-and the best k value selection for varying values of k (1,3,5,7,9,11).…”

“…Electrocardiogram (ECG) signals are extremely important in diagnosing abnormalities, such as cardiac arrhythmia [6] and heart failure. Therefore, in recent decades, there has been a significant increase in interest in the field of automatic classification of cardiovascular disorders, including AF and CHF, based on ECG signals and machine learning approaches [7][8][9][10][11][12][13]. Rizal et al used the Hjorth descriptor approach to evaluate ECG signals based on activity, mobility, and complexity features [7,8].…”