1. Conroy RM, Pyörälä K, Fitzgerald AP, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003; 24:987–1003.
Article
2. Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, May M, Brindle P. Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. BMJ. 2007; 335:136.
Article
3. D'Agostino RB Sr, Grundy S, Sullivan LM, Wilson P; CHD Risk Prediction Group. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA. 2001; 286:180–187.
4. Goldstein BA, Navar AM, Carter RE. Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges. Eur Heart J. 2017; 38:1805–1814.
Article
5. Deo RC. Machine learning in medicine. Circulation. 2015; 132:1920–1930.
Article
6. Narain R, Saxena S, Goyal AK. Cardiovascular risk prediction: a comparative study of Framingham and quantum neural network based approach. Patient Prefer Adherence. 2016; 10:1259–1270.
Article
7. Khatibi V, Montazer GA. A fuzzy-evidential hybrid inference engine for coronary heart disease risk assessment. Expert Syst Appl. 2010; 37:8536–8542.
Article
8. Kukar M, Kononenko I, Grošelj C, Kralj K, Fettich J. Analysing and improving the diagnosis of ischaemic heart disease with machine learning. Artif Intell Med. 1999; 16:25–50.
Article
9. Seong SC, Kim YY, Park SK, et al. Cohort profile: the National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) in Korea. BMJ Open. 2017; 7:e016640.
Article
10. Seong SC, Kim YY, Khang YH, et al. Data resource profile: the National Health Information Database of the National Health Insurance Service in South Korea. Int J Epidemiol. 2017; 46:799–800.
11. Hofman A, Brusselle GG, Darwish Murad S, et al. The Rotterdam Study: 2016 objectives and design update. Eur J Epidemiol. 2015; 30:661–708.
Article
12. National Health Insurance Service. National Health Screening Statistical Yearbook 2014. Wonju: National Health Insurance Service;2015.
13. López V, Fernández A, García S, Palade V, Herrera F. An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics. Inf Sci. 2013; 250:113–141.
Article
14. Cho IJ, Sung JM, Chang HJ, Chung N, Kim HC. Incremental value of repeated risk factor measurements for cardiovascular disease prediction in middle-aged Korean adults: results from the NHIS-HEALS (National Health Insurance System-National Health Screening Cohort). Circ Cardiovasc Qual Outcomes. 2017; 10:004197.
Article
15. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997; 9:1735–1780.
Article
16. Liao L, Ahn HI. Combining deep learning and survival analysis for asset health management. Int J Progn Health Manag. 2016; 7:020.
Article
17. Lloyd-Jones DM, Leip EP, Larson MG, et al. Prediction of lifetime risk for cardiovascular disease by risk factor burden at 50 years of age. Circulation. 2006; 113:791–798.
Article
18. Pencina MJ, D'Agostino RB Sr, Larson MG, Massaro JM, Vasan RS. Predicting the 30-year risk of cardiovascular disease: the Framingham heart study. Circulation. 2009; 119:3078–3084.
19. Ramsay SE, Morris RW, Whincup PH, Papacosta AO, Thomas MC, Wannamethee SG. Prediction of coronary heart disease risk by Framingham and SCORE risk assessments varies by socioeconomic position: results from a study in British men. Eur J Cardiovasc Prev Rehabil. 2011; 18:186–193.
Article
20. Murphy TP, Dhangana R, Pencina MJ, D'Agostino RB Sr. Ankle-brachial index and cardiovascular risk prediction: an analysis of 11,594 individuals with 10-year follow-up. Atherosclerosis. 2012; 220:160–167.
Article
21. Paynter NP, Chasman DI, Buring JE, Shiffman D, Cook NR, Ridker PM. Cardiovascular disease risk prediction with and without knowledge of genetic variation at chromosome 9p21.3. Ann Intern Med. 2009; 150:65–72.
Article
22. Polonsky TS, McClelland RL, Jorgensen NW, et al. Coronary artery calcium score and risk classification for coronary heart disease prediction. JAMA. 2010; 303:1610–1616.
Article
23. Hinton G, Deng L, Yu D, et al. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag. 2012; 29:82–97.
Article
24. Kennedy EH, Wiitala WL, Hayward RA, Sussman JB. Improved cardiovascular risk prediction using nonparametric regression and electronic health record data. Med Care. 2013; 51:251–258.
Article
25. Waljee AK, Higgins PD. Machine learning in medicine: a primer for physicians. Am J Gastroenterol. 2010; 105:1224–1226.
Article
26. Jung K, Shah NH. Implications of non-stationarity on predictive modeling using EHRs. J Biomed Inform. 2015; 58:168–174.
Article
27. Ross EG, Shah NH, Dalman RL, Nead KT, Cooke JP, Leeper NJ. The use of machine learning for the identification of peripheral artery disease and future mortality risk. J Vasc Surg. 2016; 64:1515–1522.e3.
Article
28. Antman EM, Loscalzo J. Precision medicine in cardiology. Nat Rev Cardiol. 2016; 13:591–602.
Article