Cardiovascular Disease Prediction by Machine Learning Algorithms Based on Cytokines in Kazakhs of China

References

• Zhou M, Wang H, Zhu J, et al. Cause-specific mortality for 240 causes in China during 1990–2013: a systematic subnational analysis for the Global Burden of Disease Study 2013. Lancet. 2016;387(10015):251–272. doi:10.1016/S0140-6736(15)00551-626510778
   PubMed Web of Science ®Google Scholar
• Collaborators GMaCoD. Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015;385(9963):117–171. doi:10.1016/S0140-6736(14)61682-225530442
   PubMed Web of Science ®Google Scholar
• 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(23):1805–1814. doi:10.1093/eurheartj/ehw30227436868
   PubMed Web of Science ®Google Scholar
• Joint Task Force for Guideline on the Assessment and Management of Cardiovascular Risk in China. [Guideline on the Assessment and Management of Cardiovascular Risk in China]. Chin J Prev Med. 2019;53(1):13.
   Google Scholar
• Goff DC Jr., Lloyd-Jones DM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association task force on practice guidelines. Circulation. 2014;129(25 Suppl 2):S49–S73. doi:10.1161/01.cir.0000437741.48606.9824222018
   PubMed Web of Science ®Google Scholar
• D’Agostino RB Sr., Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743–753. doi:10.1161/CIRCULATIONAHA.107.69957918212285
   PubMed Web of Science ®Google Scholar
• Cook NR, Ridker PM. Calibration of the pooled cohort equations for atherosclerotic cardiovascular disease: an update. Ann Intern Med. 2016;165(11):786–794. doi:10.7326/M16-173927723890
   PubMed Web of Science ®Google Scholar
• Jung KJ, Jang Y, Oh DJ, et al. The ACC/AHA 2013 pooled cohort equations compared to a Korean risk prediction model for atherosclerotic cardiovascular disease. Atherosclerosis. 2015;242(1):367–375. doi:10.1016/j.atherosclerosis.2015.07.03326255683
   PubMed Web of Science ®Google Scholar
• Lee CH, Woo YC, Lam JK, et al. Validation of the Pooled Cohort equations in a long-term cohort study of Hong Kong Chinese. J Clin Lipidol. 2015;9(5):640–646.e642. doi:10.1016/j.jacl.2015.06.00526350809
   PubMed Web of Science ®Google Scholar
• Yang X, Li J, Hu D, et al. Predicting the 10-year risks of atherosclerotic cardiovascular disease in Chinese population: the China-PAR project (prediction for ASCVD risk in China). Circulation. 2016;134(19):1430–1440. doi:10.1161/CIRCULATIONAHA.116.02236727682885
   PubMed Web of Science ®Google Scholar
• Ambale-Venkatesh B, Yang X, Wu CO, et al. Cardiovascular event prediction by machine learning: the Multi-Ethnic Study of Atherosclerosis. Circ Res. 2017;121(9):1092–1101. doi:10.1161/CIRCRESAHA.117.31131228794054
   PubMed Web of Science ®Google Scholar
• Mortazavi BJ, Downing NS, Bucholz EM, et al. Analysis of machine learning techniques for heart failure readmissions. Circ Cardiovasc Qual Outcomes. 2016;9(6):629–640. doi:10.1161/CIRCOUTCOMES.116.00303928263938
   PubMed Web of Science ®Google Scholar
• 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(5):1515–1522.e1513. doi:10.1016/j.jvs.2016.04.02627266594
   PubMed Web of Science ®Google Scholar
• Berikol GB, Yildiz O, Özcan IT. Diagnosis of acute coronary syndrome with a support vector machine. J Med Syst. 2016;40(4):84. doi:10.1007/s10916-016-0432-626815338
   PubMed Web of Science ®Google Scholar
• Arslan AK, Colak C, Sarihan ME. Different medical data mining approaches based prediction of ischemic stroke. Comput Methods Programs Biomed. 2016;130:87–92. doi:10.1016/j.cmpb.2016.03.02227208524
   PubMed Web of Science ®Google Scholar
• Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol. 2019;110:12–22. doi:10.1016/j.jclinepi.2019.02.00430763612
   PubMed Web of Science ®Google Scholar
• Mao L, Zhang X, Hu Y, et al. Nomogram based on cytokines for cardiovascular diseases in Xinjiang Kazakhs. Mediators Inflamm. 2019;2019:4756295. doi:10.1155/2019/475629531191115
   PubMed Web of Science ®Google Scholar
• Alberti KG, Zimmet P, Shaw J. The metabolic syndrome–a new worldwide definition. Lancet. 2005;366(9491):1059–1062. doi:10.1016/S0140-6736(05)67402-816182882
   PubMed Web of Science ®Google Scholar
• Joint Committee for Developing Chinese guidelines on Prevention and Treatment of Dyslipidemia in Adults. [Chinese guidelines on prevention and treatment of dyslipidemia in adults]. Zhonghua Xin Xue Guan Bing Za Zhi. 2007 May;35(5):390–419. Chinese. 
   Google Scholar
• Bergman RN. A better index of body adiposity. Obesity (Silver Spring). 2012;20(6):1135. doi:10.1038/oby.2012.9922627975
   PubMed Web of Science ®Google Scholar
• Jiang Y, Ma R, Guo H, et al. External validation of three atherosclerotic cardiovascular disease risk equations in rural areas of Xinjiang, China. BMC Public Health. 2020;20(1):1471. doi:10.1186/s12889-020-09579-432993590
   PubMed Web of Science ®Google Scholar
• Quinlan R. Induction of decision trees. Mach Learn. 1986;1(1):81–106. doi:10.1007/BF00116251
   Google Scholar
• Breiman L. Random forests. Mach Learn. 2001;45(1):5–32. doi:10.1023/A:1010933404324
   Web of Science ®Google Scholar
• Altman SN. An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat. 1992;46(3):175–185.
   Web of Science ®Google Scholar
• Zhang H. The optimality of Naive Bayes. In: International Flairs Conference; 2004.
   Google Scholar
• Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273–297. doi:10.1007/BF00994018
   Web of Science ®Google Scholar
• Chen T, Guestrin C. XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2016.
   Google Scholar
• Li L, Liu ZP. Biomarker discovery for predicting spontaneous preterm birth from gene expression data by regularized logistic regression. Comput Struct Biotechnol J. 2020;18:3434–3446. doi:10.1016/j.csbj.2020.10.02833294138
   PubMed Web of Science ®Google Scholar
• Mockus J, Tiesis V, Zilinskas A. The Application of Bayesian Methods for Seeking the Extremum. 1978.
   Google Scholar
• Platt J. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv Large Margin Classif. 2000;10.
   Google Scholar
• DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach (0006-341X (Print)). Biometrics. 1988:837–45.3203132
   PubMed Web of Science ®Google Scholar
• Rufibach K. Use of brier score to assess binary predictions. J Clin Epidemiol. 2010;63(8):938–939; author reply 939. doi:10.1016/j.jclinepi.2009.11.00920189763
   PubMed Web of Science ®Google Scholar
• Lemeshow S, Hosmer DW Jr. A review of goodness of fit statistics for use in the development of logistic regression models. Am J Epidemiol. 1982;115(1):92–106. doi:10.1093/oxfordjournals.aje.a1132847055134
   PubMed Web of Science ®Google Scholar
• Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006;26(6):565–574. doi:10.1177/0272989X0629536117099194
   PubMed Web of Science ®Google Scholar
• Moons KG, Altman DG, Reitsma JB, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015;162(1):W1–73. doi:10.7326/M14-069825560730
   PubMed Web of Science ®Google Scholar
• Hae H, Kang SJ, Kim WJ, et al. Machine learning assessment of myocardial ischemia using angiography: development and retrospective validation. PLoS Med. 2018;15(11):e1002693. doi:10.1371/journal.pmed.100269330422987
   PubMed Web of Science ®Google Scholar
• van der Ploeg T, Austin PC, Steyerberg EW. Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints. BMC Med Res Methodol. 2014;14:137. doi:10.1186/1471-2288-14-13725532820
   PubMed Web of Science ®Google Scholar
• Kakadiaris IA, Vrigkas M, Yen AA, Kuznetsova T, Budoff M, Naghavi M. Machine learning outperforms ACC/AHA CVD risk calculator in MESA. J Am Heart Assoc. 2018;7(22):e009476–e009476. doi:10.1161/JAHA.118.00947630571498
   PubMed Web of Science ®Google Scholar
• Yang L, Wu H, Jin X, et al. Study of cardiovascular disease prediction model based on random forest in eastern China. Sci Rep. 2020;10(1):5245. doi:10.1038/s41598-020-62133-532251324
   PubMed Web of Science ®Google Scholar
• Moliner-Urdiales D, Artero EG, Lee D-C, España-Romero V, Sui X, Blair SN. Body adiposity index and all-cause and cardiovascular disease mortality in men. Obesity (Silver Spring). 2013;21(9):1870–1876. doi:10.1002/oby.2039923512375
   PubMed Web of Science ®Google Scholar
• Nimkuntod P, Tongdee P. Plasma low-density lipoprotein cholesterol/high-density lipoprotein cholesterol concentration ratio and early marker of carotid artery atherosclerosis. J Med Assoc Thai. 2015;98(Suppl 4):S58–63.
   Google Scholar
• Chen QJ, Lai HM, Chen BD, et al. Appropriate LDL-C-to-HDL-C ratio cutoffs for categorization of cardiovascular disease risk factors among uygur adults in Xinjiang, China. Int J Environ Res Public Health. 2016;13(2):235. doi:10.3390/ijerph1302023526907312
   PubMed Web of Science ®Google Scholar
• Panagiotakos DB, Pitsavos C, Skoumas J, et al. Importance of LDL/HDL cholesterol ratio as a predictor for coronary heart disease events in patients with heterozygous familial hypercholesterolaemia: a 15-year follow-up (1987–2002). Curr Med Res Opin. 2003;19(2):89–94.12755140
   PubMed Web of Science ®Google Scholar
• Fujihara K, Suzuki H, Sato A, et al. Carotid artery plaque and LDL-to-HDL cholesterol ratio predict atherosclerotic status in coronary arteries in asymptomatic patients with type 2 diabetes mellitus. J Atheroscler Thromb. 2013;20(5):452–464. doi:10.5551/jat.1497723363982
   PubMed Web of Science ®Google Scholar
• Kosmas CE, Silverio D, Sourlas A, Montan PD, Guzman E, Garcia MJ. Anti-inflammatory therapy for cardiovascular disease. Ann Transl Med. 2019;7(7):147. doi:10.21037/atm.2019.02.3431157268
   PubMed Web of Science ®Google Scholar
• Ridker PM, Paynter NP, Rifai N, Gaziano JM, Cook NR. C-reactive protein and parental history improve global cardiovascular risk prediction: the reynolds risk score for men. Circulation. 2008;118(22):2243–2251. doi:10.1161/CIRCULATIONAHA.108.81425118997194
   PubMed Web of Science ®Google Scholar
• Dong Y, Wang X, Zhang L, et al. High-sensitivity C reactive protein and risk of cardiovascular disease in China-CVD study. J Epidemiol Community Health. 2019;73(2):188–192. doi:10.1136/jech-2018-21143330530521
   PubMed Web of Science ®Google Scholar
• Devaraj S, Kumaresan PR, Jialal I. C-reactive protein induces release of both endothelial microparticles and circulating endothelial cells in vitro and in vivo: further evidence of endothelial dysfunction. Clin Chem. 2011;57(12):1757–1761. doi:10.1373/clinchem.2011.16983921980169
   PubMed Web of Science ®Google Scholar
• Wilson PW, Pencina M, Jacques P, Selhub J, D’Agostino R Sr., O’Donnell CJ. C-reactive protein and reclassification of cardiovascular risk in the Framingham Heart Study. Circ Cardiovasc Qual Outcomes. 2008;1(2):92–97. doi:10.1161/CIRCOUTCOMES.108.83119820031795
   PubMed Web of Science ®Google Scholar
• Sung KC, Ryu S, Chang Y, Byrne CD, Kim SH. C-reactive protein and risk of cardiovascular and all-cause mortality in 268,803 East Asians. Eur Heart J. 2014;35(27):1809–1816. doi:10.1093/eurheartj/ehu05924569028
   PubMed Web of Science ®Google Scholar
• Montecucco F, Mach F. New evidences for C-reactive protein (CRP) deposits in the arterial intima as a cardiovascular risk factor. Clin Interv Aging. 2008;3(2):341–349. doi:10.2147/CIA.S270618686755
   PubMed Web of Science ®Google Scholar
• Badimon L, Peña E, Arderiu G, et al. C-reactive protein in atherothrombosis and angiogenesis. Front Immunol. 2018;9:430. doi:10.3389/fimmu.2018.0043029552019
   PubMed Web of Science ®Google Scholar
• Zhang B, Li XL, Zhao CR, Pan CL, Zhang Z. Interleukin-6 as a predictor of the risk of cardiovascular disease: a meta-analysis of prospective epidemiological studies. Immunol Invest. 2018;47(7):689–699. doi:10.1080/08820139.2018.148003429873573
   PubMed Web of Science ®Google Scholar
• Woodward L, Akoumianakis I, Antoniades C. Unravelling the adiponectin paradox: novel roles of adiponectin in the regulation of cardiovascular disease. Br J Pharmacol. 2017;174(22):4007–4020. doi:10.1111/bph.1361927629236
   PubMed Web of Science ®Google Scholar
• Ebrahimi-Mamaeghani M, Mohammadi S, Arefhosseini SR, Fallah P, Bazi Z. Adiponectin as a potential biomarker of vascular disease. Vasc Health Risk Manag. 2015;11:55–70. doi:10.2147/VHRM.S4875325653535
   PubMedGoogle Scholar
• Kumada M, Kihara S, Ouchi N, et al. Adiponectin specifically increased tissue inhibitor of metalloproteinase-1 through interleukin-10 expression in human macrophages. Circulation. 2004;109(17):2046–2049. doi:10.1161/01.CIR.0000127953.98131.ED15096450
   PubMed Web of Science ®Google Scholar
• Hao G, Li W, Guo R, et al. Serum total adiponectin level and the risk of cardiovascular disease in general population: a meta-analysis of 17 prospective studies. Atherosclerosis. 2013;228(1):29–35. doi:10.1016/j.atherosclerosis.2013.02.01823489345
   PubMed Web of Science ®Google Scholar
• Nomura SO, Karger AB, Weir NL, Duprez DA, Tsai MY. Free fatty acids, cardiovascular disease, and mortality in the Multi-Ethnic Study of Atherosclerosis. J Clin Lipidol. 2020;14(4):531–541. doi:10.1016/j.jacl.2020.06.00532651087
   PubMed Web of Science ®Google Scholar
• Mathew M, Tay E, Cusi K. Elevated plasma free fatty acids increase cardiovascular risk by inducing plasma biomarkers of endothelial activation, myeloperoxidase and PAI-1 in healthy subjects. Cardiovasc Diabetol. 2010;9(1):9. doi:10.1186/1475-2840-9-920158910
   PubMed Web of Science ®Google Scholar
• Khawaja O, Bartz TM, Ix JH, et al. Plasma free fatty acids and risk of atrial fibrillation (from the Cardiovascular Health Study). Am J Cardiol. 2012;110(2):212–216. doi:10.1016/j.amjcard.2012.03.01022503582
   PubMed Web of Science ®Google Scholar
• Fagot-Campagna A, Balkau B, Simon D, et al. High free fatty acid concentration: an independent risk factor for hypertension in the Paris Prospective Study. Int J Epidemiol. 1998;27(5):808–813. doi:10.1093/ije/27.5.8089839737
   PubMed Web of Science ®Google Scholar
• Tripathy D, Mohanty P, Dhindsa S, et al. Elevation of free fatty acids induces inflammation and impairs vascular reactivity in healthy subjects. Diabetes. 2003;52(12):2882–2887. doi:10.2337/diabetes.52.12.288214633847
   PubMed Web of Science ®Google Scholar
• Zhang HW, Zhao X, Guo YL, et al. Free fatty acids and cardiovascular outcome: a Chinese cohort study on stable coronary artery disease. Nutr Metab (Lond). 2017;14(1):41. doi:10.1186/s12986-017-0195-128674554
   PubMed Web of Science ®Google Scholar
• Johnson KW, Torres Soto J, Glicksberg BS, et al. Artificial intelligence in cardiology. J Am Coll Cardiol. 2018;71(23):2668–2679. doi:10.1016/j.jacc.2018.03.52129880128
   PubMed Web of Science ®Google Scholar
• Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol. 2017;69(21):2657–2664. doi:10.1016/j.jacc.2017.03.57128545640
   PubMed Web of Science ®Google Scholar
• Krittanawong C, Johnson KW, Rosenson RS, et al. Deep learning for cardiovascular medicine: a practical primer. Eur Heart J. 2019;40(25):2058–2073. doi:10.1093/eurheartj/ehz05630815669
   PubMed Web of Science ®Google Scholar
• Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–2410. doi:10.1001/jama.2016.1721627898976
   PubMed Web of Science ®Google Scholar
• Krittanawong C, Tunhasiriwet A, Zhang H, Wang Z, Aydar M, Kitai T. Deep learning with unsupervised feature in echocardiographic imaging. J Am Coll Cardiol. 2017;69(16):2100–2101. doi:10.1016/j.jacc.2016.12.047
   Web of Science ®Google Scholar
• Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–118. doi:10.1038/nature2105628117445
   PubMed Web of Science ®Google Scholar
• Blagus R, Lusa L. SMOTE for high-dimensional class-imbalanced data. BMC Bioinform. 2013;14(1):106. doi:10.1186/1471-2105-14-106
   PubMed Web of Science ®Google Scholar
• Li DC, Hu SC, Lin LS, Yeh CW, Zou Q. Detecting representative data and generating synthetic samples to improve learning accuracy with imbalanced data sets. PLoS One. 2017;12(8):e0181853. doi:10.1371/journal.pone.018185328771522
   PubMed Web of Science ®Google Scholar
• Smith MR, Martinez T, Giraud-Carrier C. An instance level analysis of data complexity. Mach Learn. 2014;95(2):225–256. doi:10.1007/s10994-013-5422-z
   Web of Science ®Google Scholar
• Han H, Wang W-Y, Mao B-H. Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Paper presented at: Advances in Intelligent Computing 2005; 2005; Berlin, Heidelberg.
   Google Scholar