Machine learning prediction in cardiovascular diseases: a meta-analysis

Sripal Bangalore,Scott Kaplin,Hafeez Ul Hassan Virk,Chayakrit Krittanawong,Jonathan L. Halperin,Rachel Pinotti,HongJu Zhang,Takeshi Kitai,Zhen Wang,Kipp W. Johnson,Bharat Narasimhan,W. H. Wilson Tang,Usman Baber

doi:10.1038/s41598-020-72685-1

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.

Highlights

Several machine learning (ML) algorithms have been increasingly utilized for cardiovascular disease prediction
An acceptable cutoff for area under the curve (AUC) to be used in clinical practice, interpretation of the cutoff, and the appropriate/best algorithms to be applied in cardiovascular datasets remain to be evaluated
To the best of our knowledge, this is the first and largest novel meta-analytic approach in ML research to date, which drew from an extensive number of studies that included over one million participants, reporting ML algorithms prediction in cardiovascular diseases

Summary

Introduction

Several machine learning (ML) algorithms have been increasingly utilized for cardiovascular disease prediction. 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. Machine learning (ML) is a branch of artificial intelligence (AI) that is increasingly utilized within the field of cardiovascular medicine It is essentially how computers make sense of data and decide or classify a task with or without human supervision. -called boosting algorithms used for prediction and classification have been applied to the identification and processing of spam email. Systematic review and meta-analysis, the foundation of modern evidence-based medicine, have to be performed in order to evaluate the existing ML algorithm in cardiovascular disease prediction. We performed the first systematic review and meta-analysis of ML research over a million patients in cardiovascular diseases

Objectives

Methods

Results

Discussion

Conclusion