Chatterjee S, Khunti K, Davies MJ. Type 2 diabetes. Lancet. 2017;389(10085):2239–51.
Dinh A, Miertschin S, Young A, Mohanty SD. A data-driven approach to predicting diabetes and cardiovascular disease with machine learning. BMC Med Inform Decis Mak. 2019;19(1):211.
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):1805–14.
Churpek MM, Yuen TC, Winslow C, Meltzer DO, Kattan MW, Edelson DP. Multicenter comparison of machine learning methods and conventional regression for predicting clinical deterioration on the wards. Crit Care Med. 2016;44(2):368–74.
Gupta S, Saluja K, Goyal A, Vajpayee A, Tiwari V. Comparing the performance of machine learning algorithms using estimated accuracy. Meas Sens. 2022;24:100432.
Joseph JJ, Deedwania P, Acharya T, Aguilar D, Bhatt DL, Chyun DA, et al. Comprehensive management of cardiovascular risk factors for adults with type 2 diabetes: a scientific statement from the American Heart Association. Circulation. 2022;145(9):e722–59.
Ayala A, Muñoz MF, Argüelles S. Lipid peroxidation: production, metabolism, and signaling mechanisms of malondialdehyde and 4-hydroxy-2-nonenal. Oxidative Med Cell Longev. 2014;2014:360438.
Kayama Y, Raaz U, Jagger A, Adam M, Schellinger IN, Sakamoto M, et al. Diabetic cardiovascular disease induced by oxidative stress. Int J Mol Sci. 2015;16(10):25234–63.
Su L-J, Zhang J-H, Gomez H, Murugan R, Hong X, Xu D, et al. Reactive oxygen species-induced lipid peroxidation in apoptosis, autophagy, and ferroptosis. Oxidative Med Cell Longev. 2019;2019:5080843.
Pratt DA, Tallman KA, Porter NA. Free radical oxidation of polyunsaturated lipids: new mechanistic insights and the development of peroxyl radical clocks. Acc Chem Res. 2011;44(6):458–67.
Zielinski ZAM, Pratt DA. Lipid peroxidation: kinetics, mechanisms, and products. J Org Chem. 2017;82(6):2817–25.
Ito F, Sono Y, Ito T. Measurement and clinical significance of lipid peroxidation as a biomarker of oxidative stress: oxidative stress in diabetes, atherosclerosis, and chronic inflammation. Antioxidants. 2019;8(3):72.
Dalakleidi K, Zarkogianni K, Thanopoulou A, Nikita K. Comparative assessment of statistical and machine learning techniques towards estimating the risk of developing type 2 diabetes and cardiovascular complications. Expert Syst. 2017;34(6):e12214.
Nicolucci A, Romeo L, Bernardini M, Vespasiani M, Rossi MC, Petrelli M, et al. Prediction of complications of type 2 diabetes: a machine learning approach. Diabetes Res Clin Pract. 2022;190:110013.
Li Q, Campan A, Ren A, Eid WE. Automating and improving cardiovascular disease prediction using machine learning and EMR data features from a regional healthcare system. Int J Med Inform. 2022;163:104786.
Edward JA, Josey K, Bahn G, Caplan L, Reusch JEB, Reaven P, et al. Heterogeneous treatment effects of intensive glycemic control on major adverse cardiovascular events in the ACCORD and VADT trials: a machine-learning analysis. Cardiovasc Diabetol. 2022;21(1):58.
Jiang Y, Yang Z-G, Wang J, Shi R, Han P-L, Qian W-L, et al. Unsupervised machine learning based on clinical factors for the detection of coronary artery atherosclerosis in type 2 diabetes mellitus. Cardiovasc Diabetol. 2022;21(1):259.
Drożdż K, Nabrdalik K, Kwiendacz H, Hendel M, Olejarz A, Tomasik A, et al. Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: a machine learning approach. Cardiovasc Diabetol. 2022;21(1):240.
Hahn S-J, Kim S, Choi YS, Lee J, Kang J. Prediction of type 2 diabetes using genome-wide polygenic risk score and metabolic profiles: a machine learning analysis of population-based 10-year prospective cohort study. eBioMedicine. 2022;86:104383.
Damen JAAG, Hooft L, Schuit E, Debray TPA, Collins GS, Tzoulaki I, et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ. 2016;353:i2416.
Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015;4(1):1.
Riva JJ, Malik KMP, Burnie SJ, Endicott AR, Busse JW. What is your research question? An introduction to the PICOT format for clinicians. J Can Chiropr Assoc. 2012;56(3):167–71.
Moons KGM, de Groot JAH, Bouwmeester W, Vergouwe Y, Mallett S, Altman DG, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med. 2014;11(10):e1001744.
Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med. 2015;162(1):55–63.
Moons KGM, Altman DG, Reitsma JB, Ioannidis JPA, Macaskill P, Steyerberg EW, 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–W73.
Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med. 2019;170(1):51–8.
Parfitt VJ, Desomeaux K, Bolton CH, Hartog M. Effects of high monounsaturated and polyunsaturated fat diets on plasma lipoproteins and lipid peroxidation in type 2 diabetes mellitus. Diabet Med. 1994;11(1):85–91.
Chu H, Chen L, Yang X, Qiu X, Qiao Z, Song X, et al. Roles of anxiety and depression in predicting cardiovascular disease among patients with type 2 diabetes mellitus: a machine learning approach. Front Psychol. 2021;12:1189.
Zarkogianni K, Athanasiou M, Thanopoulou AC. Comparison of machine learning approaches toward assessing the risk of developing cardiovascular disease as a long-term diabetes complication. IEEE J Biomed Health Inform. 2018;22(5):1637–47.
Derevitskii IV, Kovalchuk SV. Machine learning-based predictive modeling of complications of chronic diabetes. Procedia Comput Sci. 2020;178:274–83.
Athanasiou M, Sfrintzeri K, Zarkogianni K, Thanopoulou AC, Nikita KS. An explainable XGBoost-based approach towards assessing the risk of cardiovascular disease in patients with Type 2 diabetes mellitus. ArXiv. 2020. arXiv:2009.06629.
Dworzynski P, Aasbrenn M, Rostgaard K, Melbye M, Gerds TA, Hjalgrim H, et al. Nationwide prediction of type 2 diabetes comorbidities. Sci Rep. 2020;10(1):1776.
Mei J, Xia E. Knowledge learning symbiosis for developing risk prediction models from regional EHR repositories. Stud Health Technol Inform. 2019;264:258–62.
Nowak C, Carlsson AC, Ostgren CJ, Nystrom FH, Alam M, Feldreich T, et al. Multiplex proteomics for prediction of major cardiovascular events in type 2 diabetes. Diabetologia. 2018;61(8):1748–57.
Hossain ME, Uddin S, Khan A. Network analytics and machine learning for predictive risk modelling of cardiovascular disease in patients with type 2 diabetes. Expert Syst Appl. 2021;164:113918.
Miao L, Guo X, Abbas HT, Qaraqe KA, Abbasi QH, editors. Using machine learning to predict the future development of disease. In: 2020 international conference on UK-China emerging technologies (UCET), 2020 20–21 Aug; 2020.
Ahmad WAW. Annual report of the NCVD-ACS registry, 2018–2019. National Cardiovascular Disease Database; 2022.
International Diabetes Federation. IDF diabetes atlas 2021: IDF; 2021.
Khan MAB, Hashim MJ, King JK, Govender RD, Mustafa H, Al Kaabi J. Epidemiology of type 2 diabetes—global burden of Disease and Forecasted Trends. J Epidemiol Glob Health. 2020;10(1):107–11.
American Diabetes Association. 2. Classification and diagnosis of diabetes: standards of medical care in diabetes-2020; 2020.
He H, Garcia EA. Learning from imbalanced data. IEEE Trans Knowl Data Eng. 2009;21(9):1263–84.
Holte RC, Acker L, Porter BW, editors. Concept learning and the problem of small disjuncts. In: IJCAI; 1989.
Mahmood SS, Levy D, Vasan RS, Wang TJ. The Framingham heart study and the epidemiology of cardiovascular disease: a historical perspective. Lancet. 2014;383(9921):999–1008.
Garcia MJ, McNamara PM, Gordon T, Kannel WB. Morbidity and mortality in diabetics in the Framingham population. Sixteen year follow-up study. Diabetes. 1974;23(2):105–11.
Kannel WB, Hjortland M, Castelli WP. Role of diabetes in congestive heart failure: the Framingham study. Am J Cardiol. 1974;34(1):29–34.
Williams BA, Geba D, Cordova JM, Shetty SS. A risk prediction model for heart failure hospitalization in type 2 diabetes mellitus. Clin Cardiol. 2020;43(3):275–83.
Pylypchuk R, Wells S, Kerr A, Poppe K, Harwood M, Mehta S, et al. Cardiovascular risk prediction in type 2 diabetes before and after widespread screening: a derivation and validation study. Lancet. 2021;397(10291):2264–74.
Chhatwal J, Alagoz O, Lindstrom MJ, Kahn CE Jr, Shaffer KA, Burnside ES. A logistic regression model based on the national mammography database format to aid breast cancer diagnosis. AJR Am J Roentgenol. 2009;192(4):1117–27.
Dahlrot RH, Bangsø JA, Petersen JK, Rosager AM, Sørensen MD, Reifenberger G, et al. Prognostic role of Ki-67 in glioblastomas excluding contribution from non-neoplastic cells. Sci Rep. 2021;11(1):17918.
Fuster-Garcia E, Lorente Estellés D, Álvarez-Torres MDM, Juan-Albarracín J, Chelebian E, Rovira A, et al. MGMT methylation may benefit overall survival in patients with moderately vascularized glioblastomas. Eur Radiol. 2021;31(3):1738–47.
Bzdok D, Altman N, Krzywinski M. Statistics versus machine learning. Nat Methods. 2018;15(4):233–4.
Ortega FB, Lavie CJ, Blair SN. Obesity and cardiovascular disease. Circul Res. 2016;118(11):1752–70.
Ley SH, Hamdy O, Mohan V, Hu FB. Prevention and management of type 2 diabetes: dietary components and nutritional strategies. Lancet. 2014;383(9933):1999–2007.
Gray N, Picone G, Sloan F, Yashkin A. Relation between BMI and diabetes mellitus and its complications among US older adults. South Med J. 2015;108(1):29–36.
Kolber MR, Scrimshaw C. Family history of cardiovascular disease. Can Fam Physician. 2014;60(11):1016.
Valerio L, Peters RJ, Zwinderman AH, Pinto-Sietsma SJ. Association of family history with cardiovascular disease in hypertensive individuals in a multiethnic population. J Am Heart Assoc. 2016;5(12):e004260.
Vona R, Gambardella L, Cittadini C, Straface E, Pietraforte D. Biomarkers of oxidative stress in metabolic syndrome and associated diseases. Oxidative Med Cell Longev. 2019;2019:8267234.
Lundberg WO. Lipids of biologic importance: peroxidation products and inclusion compounds of lipids. Am J Clin Nutr. 1958;6(6):601–3.
Bigagli E, Lodovici M. Circulating oxidative stress biomarkers in clinical studies on type 2 diabetes and its complications. Oxidative Med Cell Longev. 2019;2019:5953685.
Saito T, Rehmsmeier M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE. 2015;10(3):e0118432.
Ho SY, Phua K, Wong L, Bin Goh WW. Extensions of the external validation for checking learned model interpretability and generalizability. Patterns. 2020;1(8):100129.