- •Machine learning (ML) can address the limitations of regression-based models.
- •The ML-derived model exhibited better predictions than an established model.
- •The ML-based approach can be used for risk stratification in clinical practice.
Background and aims
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- European society of cardiology: cardiovascular disease statistics 2019.Eur. Heart J. 2020; 41: 12-85
- Age- and sex-related differences in all-cause mortality risk based on coronary computed tomography angiography findings results from the International Multicenter CONFIRM (Coronary CT Angiography Evaluation for Clinical Outcomes: an International Multicenter Registry) of 23,854 patients without known coronary artery disease.J. Am. Coll. Cardiol. 2011; 58: 849-860
- ESC/EACTS Guidelines on myocardial revascularization.Eur. Heart J. 2018; 40 (2019): 87-165
- Anatomical and clinical characteristics to guide decision making between coronary artery bypass surgery and percutaneous coronary intervention for individual patients: development and validation of SYNTAX score II.Lancet. 2013; 381: 639-650
- Individual long-term mortality prediction following either coronary stenting or bypass surgery in patients with multivessel and/or unprotected left main disease: an external validation of the SYNTAX score II model in the 1,480 patients of the BEST and PRECOMBAT randomized controlled trials.JACC Cardiovasc. Interv. 2016; 9: 1564-1572
- Impact of the SYNTAX scores I and II in patients with diabetes and multivessel coronary disease: a pooled analysis of patient level data from the SYNTAX, PRECOMBAT, and BEST trials.Eur. Heart J. 2017; 38: 1969-1977
- Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.Eur. Heart J. 2017; 38: 1805-1814
- Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis.Eur. Heart J. 2017; 38: 500-507
- Characteristics associated with decreased or increased mortality risk from glycemic therapy among patients with type 2 diabetes and high cardiovascular risk: machine learning analysis of the ACCORD trial.Diabetes Care. 2018; 41: 604-612
- Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10 019 patients.Eur. Heart J. 2019; 40: 1069-1077
- Circulating tumor DNA methylation profiles enable early diagnosis, prognosis prediction, and screening for colorectal cancer.Sci. Transl. Med. 2020; 12
- Implications of N-terminal pro-B-type natriuretic peptide in patients with three-vessel disease.Eur. Heart J. 2019; 40: 3397-3405
- American college of cardiology F, American heart association task force on practice G, society for cardiovascular A and interventions. 2011 ACCF/AHA/SCAI guideline for percutaneous coronary intervention. A report of the American college of cardiology foundation/American heart association task force on practice guidelines and the society for cardiovascular angiography and interventions.J. Am. Coll. Cardiol. 2011; 58: e44-e122
- American college of cardiology F, American heart association task force on practice G, American association for thoracic S, society of cardiovascular A and society of thoracic S. 2011 ACCF/AHA guideline for coronary artery bypass graft surgery. A report of the American college of cardiology foundation/American heart association task force on practice guidelines. Developed in collaboration with the American association for thoracic surgery, society of cardiovascular anesthesiologists, and society of thoracic surgeons.J. Am. Coll. Cardiol. 2011; 58: e123-e210
- Feature selection for discrete and numeric class machine learning.Working Papers Series. 1999; : 1-16
- The WEKA data mining software: an update.ACM SIGKDD Explorat. Newsl. 2009; 11: 10-18
- Genetic Algorithms in Search, Optimization and Machine Learning.1th ed. Addison-Wesley, Boston, USA1989
- Random forests.Mach. Learn. 2001; 45: 5-32
- Classification and Regression Trees.Wadsworth International Group, Belmont, CA1984
- Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.Biometrics. 1988; 44: 837-845
- Percutaneous coronary intervention versus coronary-artery bypass grafting for severe coronary artery disease.N. Engl. J. Med. 2009; 360: 961-972
- A new tool for the risk stratification of patients with complex coronary artery disease: the Clinical SYNTAX Score.Circ. Cardiovasc Interv. 2010; 3: 317-326
- SYNTAX score and Clinical SYNTAX score as predictors of very long-term clinical outcomes in patients undergoing percutaneous coronary interventions: a substudy of SIRolimus-eluting stent compared with pacliTAXel-eluting stent for coronary revascularization (SIRTAX) trial.Eur. Heart J. 2011; 32: 3115-3127
- Detection of hypertrophic cardiomyopathy using a convolutional neural network-enabled electrocardiogram.J. Am. Coll. Cardiol. 2020; 75: 722-733