Analysis in performance of GM(1, 1) model in predicting blood glucose at different ranges

LU Xiangyue; WANG Yannian; LI Quanzhong

doi:10.7619/jcmp.20210757

Journal of Clinical Medicine in Practice > 2021 > 25(9): 23-28, 36. > DOI: 10.7619/jcmp.20210757

LU Xiangyue, WANG Yannian, LI Quanzhong. Analysis in performance of GM(1, 1) model in predicting blood glucose at different ranges[J]. Journal of Clinical Medicine in Practice, 2021, 25(9): 23-28, 36. DOI: 10.7619/jcmp.20210757

Citation:

PDF (5807 KB)

Analysis in performance of GM(1, 1) model in predicting blood glucose at different ranges

1.
Department of Endocrinology, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Zhengzhou, Henan, 450003
2.
School of Information Engineering, Zhengzhou University, Zhengzhou, Henan, 450001

More Information

Received Date: February 23, 2021
Available Online: May 20, 2021
Published Date: May 14, 2021

Graphical Abstract

Abstract

Abstract

Objective To evaluate performance of GM (1, 1) model in predicting blood glucose, and the predictive ability of this model for prediction of different blood glucose ranges.
Methods Seventy-two-hour blood glucose sequence of fifty patients with T2DM was selected as study objects. GM (1, 1) model was established based on the metabolic algorithm to predict blood glucose levels after 5, 15 and 30 min. Fifty patients were randomly divided into control group(n=20) and experimental group(n=30). The blood glucose range(ranging from a to b) when the prediction error was low in the control group was obtained. According to the blood glucose range of the control group, the data of the experimental group was divided into group A(< a), group B(a to b), and group C(>b). Analysis was performed by Pearson correlation analysis and nonparametric test.
Results The blood glucose ranges for prediction duration of 5 min, 15 min and 30 min in the control group were 3.4 to 11.5 mmol/L, 3.3 to 11.4 mmol/L and 3.2 to 11.4 mmol/L, respectively. Correlation analysis showed that group B at differed prediction durations had the best predictive fit (P < 0.01). Nonparametric tests indicated that there was a statistical difference in prediction error between groups at the same prediction duration (P < 0.01), and the group A had the minimum error, followed by group B, and group C had the maximum error.
Conclusion GM (1, 1) model has better predictive efficacy at blood glucose range of 3.4 to 11.4 mmol/L.
- GM(1, 1) model,
- type 2 diabetes mellitus,
- prediction of blood glucose,
- range of blood glucose

FullText(HTML)

References (20)

References

[1]	AKBAS E M, DEMIRTAS L, OZCICEK A, et al. Association of epicardial adipose tissue, neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio with diabetic nephropathy[J]. Int J Clin Exp Med, 2014, 7(7): 1794-1801. http://europepmc.org/abstract/MED/25126182
[2]	中华医学会糖尿病学分会. 中国2型糖尿病防治指南(2017年版)[J]. 中国实用内科杂志, 2018, 38(4): 292-344. https://www.cnki.com.cn/Article/CJFDTOTAL-SYNK201804009.htm
[3]	Seuring T, Archangelidi O, Suhrcke M. The economic costs of type 2 diabetes: a global systematic review[J]. Pharmacoeconomics, 2015, 33(8): 811-831. doi: 10.1007/s40273-015-0268-9
[4]	SAITI K, MACAŠ M, LHOTSKÁ L, et al. Ensemble methods in combination with compartment models for blood glucose level prediction in type 1 diabetes mellitus[J]. Comput Methods Programs Biomed, 2020, 196: 105628. doi: 10.1016/j.cmpb.2020.105628
[5]	HE J, WANG Y. Blood glucose concentration prediction based on kernel canonical correlation analysis with particle swarm optimization and error compensation[J]. Comput Methods Programs Biomed, 2020, 196: 105574. doi: 10.1016/j.cmpb.2020.105574
[6]	都承华, 龚谊承. 基于支持向量回归和核岭回归对血糖值预测的对比分析[J]. 数学的实践与认识, 2020, 50(6): 132-139. https://www.cnki.com.cn/Article/CJFDTOTAL-SSJS202006013.htm
[7]	王延年, 郭占丽, 袁进磊, 等. 基于自适应噪声完整聚合经验模态分解-极限学习机的短期血糖预测[J]. 中国生物医学工程学报, 2017, 36(6): 702-710. doi: 10.3969/j.issn.0258-8021.2017.06.010
[8]	邓聚龙. 灰色系统理论教程[M]. 武汉: 华中理工大学出版社, 1990: 122-123.
[9]	覃伟, 高敏, 沈莹, 等. 基于机器学习算法的2型糖尿病患者3个月血糖预测[J]. 中华疾病控制杂志, 2019, 23(11): 1313-1317. https://www.cnki.com.cn/Article/CJFDTOTAL-JBKZ201911003.htm
[10]	WANG Y, WEI F, SUN C, et al. The Research of Improved Grey GM(1, 1) Model to Predict the Postprandial Glucose in Type 2 Diabetes[J]. Biomed Res Int, 2016, 2016: 6837052. http://europepmc.org/articles/PMC4893588/
[11]	龚谊承, 都承华, 张艳娜, 等. 基于主成分和GBDT对血糖值的预测[J]. 数学的实践与认识, 2019, 49(14): 116-122. https://www.cnki.com.cn/Article/CJFDTOTAL-SSJS201914013.htm
[12]	VOELKER R. "artificial pancreas" is approved[J]. JAMA, 2016, 316(19): 1957. http://www.ncbi.nlm.nih.gov/pubmed/27838705
[13]	KOVATCHEV B, CHENG P, ANDERSON S M, et al. Feasibility of long-term closed-loop control: a multicenter 6-month trial of 24/7 automated insulin delivery[J]. Diabetes Technol Ther, 2017, 19(1): 18-24. doi: 10.1089/dia.2016.0333
[14]	SCHVTZ-FUHRMANN I, SCHVTZ A K, EICHNER M, et al. Two subsequent pregnancies in a woman with type 1 diabetes: artificial pancreas was a gamechanger[J]. J Diabetes Sci Technol, 2020, 14(5): 972-973. doi: 10.1177/1932296820906219
[15]	THABIT H, HARTNELL S, ALLEN J M, et al. Closed-loop insulin delivery in inpatients with type 2 diabetes: a randomised, parallel-group trial[J]. Lancet Diabetes Endocrinol, 2017, 5(2): 117-124. doi: 10.1016/S2213-8587(16)30280-7
[16]	QUINTAL A, MESSIER V, RABASA-LHORET R, et al. A critical review and analysis of ethical issues associated with the artificial pancreas[J]. Diabetes Metab, 2019, 45(1): 1-10. doi: 10.1016/j.diabet.2018.04.003
[17]	RAMLI R, REDDY M, OLIVER N. Artificial pancreas: current progress and future outlook in the treatment of type 1 diabetes[J]. Drugs, 2019, 79(10): 1089-1101. doi: 10.1007/s40265-019-01149-2
[18]	BLAUW H, KEITH-HYNES P, KOOPS R, et al. A review of safety and design requirements of the artificial pancreas[J]. Ann Biomed Eng, 2016, 44(11): 3158-3172. doi: 10.1007/s10439-016-1679-2
[19]	QIN Y F, XIAO J, CHEN X D. Physiological model and chemical modeling strategy for blood glucose prediction[J]. Chemical Industry and Engineering Progress, 2019, 38(1): 545-555.
[20]	OVIEDO S, VEHÍ J, CALM R, et al. A review of personalized blood glucose prediction strategies for T1DM patients[J]. Int J Numer Meth Biomed Engng, 2017, 33(6): e2833. doi: 10.1002/cnm.2833

[1]	HE Tao, PAN Peng, WANG Qiuye, LI Lei, QIU Shilin, QU Hongmei. Research progress on recurrence related histology of ulcerative colitis[J]. Journal of Clinical Medicine in Practice, 2022, 26(12): 135-138. DOI: 10.7619/jcmp.20214550
[2]	ZHANG Hanqing, LIU Ying, FANG Xiao. Research on drug resistance of aneuploid colon cancer cells to cisplatin[J]. Journal of Clinical Medicine in Practice, 2020, 24(1): 42-45. DOI: 10.7619/jcmp.202001011
[3]	CHEN Li. Effect observation of probiotics combined with Kangfuxin solution on treatment of patients with mild to moderate ulcerative colitis in active stage[J]. Journal of Clinical Medicine in Practice, 2019, 23(12): 92-95. DOI: 10.7619/jcmp.201912027
[4]	LIU Yuhui, SUN Qingqing, GUO Fang. Effect of Gab2 on extracellular adhesion of colon cancer cells[J]. Journal of Clinical Medicine in Practice, 2017, (15): 61-63. DOI: 10.7619/jcmp.201715016
[5]	LI Zhifeng, LIU Jinzhong, SHI Rongya, ZHAO Xiaoli, HUO Man, LIU Fang. Expressions of CDX2, COX-2 and VEGF in colon carcinoma tissues[J]. Journal of Clinical Medicine in Practice, 2017, (5): 79-82. DOI: 10.7619/jcmp.201705022
[6]	ZHAO Peilei, ZHAO Yijie, LENG Jie, GONG Weijuan. Study of NK cell activation by colon cancer cells genetically modified with NKG2D-IL-21 fusion gene ex vivo[J]. Journal of Clinical Medicine in Practice, 2016, (1): 1-4. DOI: 10.7619/jcmp.201601001
[7]	BAI Bin, YUAN Anlong, KOU Jiguang. Application of fecal calprotectin and serum C-reactive protein detections in evaluation of activity determination of ulcerative colitis[J]. Journal of Clinical Medicine in Practice, 2015, (1): 56-58. DOI: 10.7619/jcmp.201501017
[8]	XIE Min, JI Jie. Application of improved colon dialysis in the male elderly patients with leakage problems[J]. Journal of Clinical Medicine in Practice, 2014, (22): 48-50. DOI: 10.7619/jcmp.201422015
[9]	LUO Peiyuan, ZHAO Yanhong. Clinical significance and expression ofβdefensin-2 of colonic mucosa in patients with ulcerative colitis[J]. Journal of Clinical Medicine in Practice, 2014, (7): 119-121. DOI: 10.7619/jcmp.201407040
[10]	TIAN Xiaohui, HU Bin, QIN Rong, WANG Han, MENG Xiangjun. Clinical significance of IGF-1 level and VEGF expression in diagnosis and treatment of patients with colon cancer[J]. Journal of Clinical Medicine in Practice, 2014, (3): 9-11. DOI: 10.7619/jcmp.201403003

Cited By

Cited by

Periodical cited type(2)

1.	朱军，赵学臣，汪阳林，杨宝昌，董巍，王红岩，米盼盼，孔繁林，张天一. 基于ROC曲线模型下分析血清PCT水平与腰椎内固定术后感染的关系. 川北医学院学报. 2020(02): 259-263 .
2.	赵思婷，冯永洪，李真，王新强. 五味消毒饮联合闭式灌洗引流术治疗腰椎内固定术后早期感染(气滞血瘀)随机平行对照研究. 实用中医内科杂志. 2019(06): 20-23 .

Other cited types(1)

Get Citation

PDF

XML

Article views (394) PDF downloads (30) Cited by(3)

Turn off MathJax

Article Contents

Abstract

References

Analysis in performance of GM(1, 1) model in predicting blood glucose at different ranges