Machine learning prediction in cardiovascular diseases: a meta-analysis
Baylor College of Medicine · Mount Sinai Hospital · +10 more institutions
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…
Citation impact
- FWCI
- 34.78
- Percentile
- 100%
- References
- 38
Authors
13- CKChayakrit KrittanawongCorresponding
Baylor College of Medicine, Mount Sinai Hospital, Mount Sinai Hospital, Icahn School of Medicine at Mount Sinai
- HUHafeez Ul Hassan Virk
University Hospitals of Cleveland, Case Western Reserve University
- SBSripal Bangalore
New York University
- ZWZhen Wang
Mayo Clinic in Florida
- KWKipp W. Johnson
Icahn School of Medicine at Mount Sinai
Topics & keywords
- Boosting (machine learning)
- Machine learning
- Support vector machine
- Algorithm
- Medicine
- Meta-analysis
- Confidence interval
- Coronary artery disease