Risk Prediction of Diabetes Progression Using Big Data Mining with Multifarious Physical Examination Indicators

References

• World Health Organization. Definition and diagnosis of diabetes mellitus and intermediate hyperglycaemia: report of a WHO/IDF consultation; 2006.
   Google Scholar
• Rao SS, Disraeli P, McGregor T. Impaired glucose tolerance and impaired fasting glucose. Am Family Phys. 2004;69(8):1961–1968.
   PubMed Web of Science ®Google Scholar
• Huang Y, Cai X, Mai W, Li M, Hu Y. Association between prediabetes and risk of cardiovascular disease and all cause mortality: systematic review and meta-analysis. BMJ. 2016;355. doi:10.1136/bmj.i5953
   PubMedGoogle Scholar
• Paschou SA, Bletsa E, Saltiki K, et al. Sleep apnea and cardiovascular risk in patients with prediabetes and type 2 diabetes. Nutrients. 2022;14(23):4989. doi:10.3390/nu14234989
   PubMed Web of Science ®Google Scholar
• Tab ́ak AG, Herder C, Rathmann W, Brunner EJ, Kivim ̈aki M. Prediabetes: a high-risk state for diabetes development. Lancet. 2012;379(9833):2279–2290. doi:10.1016/S0140-6736(12)60283-9
   PubMed Web of Science ®Google Scholar
• American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2010;33(Supplement_1):S62–S69. doi:10.2337/dc10-S062
   PubMedGoogle Scholar
• Raj S, Rajan G. Correlation between elevated serum ferritin and hba1c in type 2 diabetes mellitus. Int J Res Med Sci. 2013;1(1):12–15. doi:10.5455/2320-6012.ijrms20130203
   Google Scholar
• Elimam H, Abdulla AM, Taha IM. Inflammatory markers and control of type 2 diabetes mellitus. Diabetes Metabol Synd. 2019;13(1):800–804. doi:10.1016/j.dsx.2018.11.061
   PubMed Web of Science ®Google Scholar
• Tamura Y, Izumiyama-Shimomura N, Kimbara Y, et al. β-cell telomere attrition in diabetes: inverse correlation between hba1c and telomere length. J Clin Endocrinol Metab. 2014;99(8):2771–2777. doi:10.1210/jc.2014-1222
   PubMed Web of Science ®Google Scholar
• Buch A, Kaur S, Nair R, Jain A. Platelet volume indices as predictive biomarkers for diabetic complications in type 2 diabetic patients. J Lab Phys. 2017;9(02):084–088. doi:10.4103/0974-2727.199625
   PubMedGoogle Scholar
• Yang H, Luo Y, Ren X, et al. Risk prediction of diabetes: big data mining with fusion of multifarious physical examination indicators. Information Fusion. 2021;75:140–149. doi:10.1016/j.inffus.2021.02.015
   Web of Science ®Google Scholar
• Xu W, Zhang J, Zhang Q, Wei X. Risk prediction of type II diabetes based on random forest model. Proceedings of the 3rd IEEE international conference on advances in electrical and electronics, information, communication and bio-informatics, AEEICB 2017; 2017.
   Google Scholar
• Mujumdar A, Vaidehi V. Diabetes prediction using ML algorithms. Procedia Comput Sci. 2019;165:292–299. doi:10.1016/j.procs.2020.01.047
   Google Scholar
• Li M, Fu X, Li D. Diabetes prediction based on XGBoost algorithm. IOP Conf Ser Mater Sci Eng. 2020;768(7):072093. doi:10.1088/1757-899X/768/7/072093
   Google Scholar
• Choi SB, Kim WJ, Yoo TK, et al. Screening for prediabetes using machine learning models. Computational and mathematical methods in medicine 2014; 2014.
   Google Scholar
• Kopitar L, Kocbek P, Cilar L, Sheikh A, Stiglic G. Early detection of type 2 diabetes mellitus using machine learning-based prediction models. Sci Rep. 2020;10(1):11981. doi:10.1038/s41598-020-68771-z
   PubMedGoogle Scholar
• Wang L, Mu Y, Zhao J, Wang X, Che H. Igrnet: a deep learning model for non-invasive, real-time diagnosis of prediabetes through electrocardiograms. Sensors. 2020;20(9):2556. doi:10.3390/s20092556
   PubMed Web of Science ®Google Scholar
• B ̈uhlmann P, Van De Geer S. Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer Science & Business Media; 2011.
   Google Scholar
• Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP. Random forest: a classification and regression tool for compound classification and qsar modeling. J Chem Inf Comput Sci. 2003;43(6):1947–1958. doi:10.1021/ci034160g
   PubMedGoogle Scholar
• Cortes C, Vapnik V. Support-vector networks. Machine Learning. 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; 2016:785–794.
   Google Scholar
• Menke A, Casagrande S, Geiss L, Cowie CC. Prevalence of and trends in diabetes among adults in the United States, 1988–2012. JAMA. 2015;314(10):1021–1029. doi:10.1001/jama.2015.10029
   PubMed Web of Science ®Google Scholar
• Bays HE, Chapman R, Grandy S; the SHIELD Investigators’ Group. The relationship of body mass index to diabetes mellitus, hypertension and dyslipidaemia: comparison of data from two national surveys. Int J Clin Pract. 2007;61(5):737–747. doi:10.1111/j.1742-1241.2007.01336.x
   PubMed Web of Science ®Google Scholar
• Zhang Y, Nie J, Zhang Y, et al. Degree of blood pressure control and incident diabetes mellitus in Chinese adults with hypertension. J Am Heart Assoc. 2020;9(16):e017015. doi:10.1161/JAHA.120.017015
   PubMed Web of Science ®Google Scholar
• Adler AI, Stratton IM, Neil HAW, et al. Association of systolic blood pressure with macrovascular and microvascular complications of type 2 diabetes (ukpds 36): prospective observational study. BMJ. 2000;321(7258):412–419. doi:10.1136/bmj.321.7258.412
   PubMed Web of Science ®Google Scholar
• Chen J, Guo HJ, Qiu S-H, et al. Identification of newly diagnosed diabetes and prediabetes using fasting plasma glucose and urinary glucose in a Chinese population: a multicenter cross-sectional study. Chinese Med J. 2018;131(14):1652–1657. doi:10.4103/0366-6999.235884
   PubMed Web of Science ®Google Scholar
• Okada R, Yasuda Y, Tsushita K, Wakai K, Hamajima N, Matsuo S. Trace proteinuria by dipstick screening is associated with metabolic syndrome, hypertension, and diabetes. Clin Experiment Nephrol. 2018;22(6):1387–1394. doi:10.1007/s10157-018-1601-3
   PubMed Web of Science ®Google Scholar
• Sabanayagam C, Shankar A, Lim S, Lee J, Tai E, Wong T. Serum c-reactive protein level and prediabetes in two asian populations. Diabetologia. 2011;54(4):767–775. doi:10.1007/s00125-011-2052-5
   PubMed Web of Science ®Google Scholar
• Tilves CM, Zmuda JM, Kuipers AL, et al. Association of lipopolysaccharide-binding protein with aging-related adiposity change and prediabetes among African ancestry men. Diabetes Care. 2016;39(3):385–391. doi:10.2337/dc15-1777
   PubMed Web of Science ®Google Scholar
• Dimova R, Chakarova N, Kirilov G, Grozeva G, Shinkov A, Tankova T. Vitamin d binding protein is related to cardiac autonomic function and metabolic status in prediabetes. Nutr Res. 2020;75:56–66. doi:10.1016/j.nutres.2019.12.008
   PubMed Web of Science ®Google Scholar
• Iyidir OT, Degertekin CK, Yilmaz BA, et al. Serum levels of fetuin a are increased in women with gestational diabetes mellitus. Arch Gynecol Obstetrics. 2015;291(4):933–937. doi:10.1007/s00404-014-3490-3
   PubMed Web of Science ®Google Scholar
• Zhou Y, Yang G, Qu C, et al. Predictive performance of lipid parameters in identifying undiagnosed diabetes and prediabetes: a cross-sectional study in eastern China. BMC Endocr Disord. 2022;22:1–9. doi:10.1186/s12902-022-00984-x
   PubMed Web of Science ®Google Scholar
• von Eckardstein A, Widmann C. High-density lipoprotein, beta cells, and diabetes. Cardiovascul Res. 2014;103(3):384–394. doi:10.1093/cvr/cvu143
   PubMed Web of Science ®Google Scholar
• Sobers-Grannum N, Murphy MM, Nielsen A, et al. Female gender is a social determinant of diabetes in the Caribbean: a systematic review and meta-analysis. PLoS One. 2015;10(5):e0126799. doi:10.1371/journal.pone.0126799
   PubMed Web of Science ®Google Scholar
• Hermans MP, Ahn SA, Rousseau MF. log(TG)/HDL-C is related to both residual cardiometabolic risk and β-cell function loss in type 2 diabetes males. Cardiovascul Diabetol. 2010;9(1):1–10. doi:10.1186/1475-2840-9-88
   PubMedGoogle Scholar
• Qian K, Zhong S, Xie K, Yu D, Yang R, Gong D-W. Hepatic alt isoenzymes are elevated in gluconeogenic conditions including diabetes and suppressed by insulin at the protein level. Diabetes Metab Res Rev. 2015;31(6):562–571. doi:10.1002/dmrr.2655
   PubMed Web of Science ®Google Scholar
• Niu H, Zhou Y. Nonlinear relationship between ast-to-alt ratio and the incidence of type 2 diabetes mellitus: a follow-up study. Int J Gene Med. 2021;Volume 14:8373–8382. doi:10.2147/IJGM.S341790
   Web of Science ®Google Scholar
• Gong D, Chen X, Yang L, et al. From normal population to prediabetes and diabetes: study of influencing factors and prediction models. Front Endocrinol. 2023;14:1225696. PMID: 37964953; PMCID: PMC10640999. doi:10.3389/fendo.2023.1225696
   PubMed Web of Science ®Google Scholar