[1]陈真诚,杨薛冰,邹春林,等.基于无创血糖的糖尿病评估[J].中国医学物理学杂志,2020,37(10):1330-1334.[doi:DOI:10.3969/j.issn.1005-202X.2020.10.020]
　CHEN Zhencheng,YANG Xuebing,ZOU Chunlin,et al.Diabetes evaluation based on noninvasive blood glucose[J].Chinese Journal of Medical Physics,2020,37(10):1330-1334.[doi:DOI:10.3969/j.issn.1005-202X.2020.10.020]
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基于无创血糖的糖尿病评估()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
37
期数:
2020年第10期
页码:
1330-1334
栏目:
医学人工智能
出版日期:
2020-10-29

文章信息/Info

Title:
Diabetes evaluation based on noninvasive blood glucose
文章编号:
1005-202X（2020）10-1330-05
作者:
陈真诚1; 杨薛冰1; 邹春林2; 严波文1; 朱健铭3; 梁永波3
1.桂林电子科技大学电子工程与自动化学院， 广西 桂林 541004； 2.广西医科大学转化医学研究中心， 广西 南宁 530021； 3.桂林电子科技大学生命与环境科学学院， 广西 桂林 541004
Author(s):
CHEN Zhencheng1;  YANG Xuebing1;  ZOU Chunlin2;  YAN Bowen1;  ZHU Jianming3;  LIANG Yongbo3
1. School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China 2. Transforming Medical Research Center, Guangxi Medical University, Nanning 530021, China 3. School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
关键词:
糖尿病; 糖化血红蛋白; 糖耐量受损; 神经网络; K-近邻算法
Keywords:
Keywords: diabetes glycosylated hemoglobin impaired glucose tolerance neural network K-nearest neighbor algorithm
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2020.10.020
文献标志码:
A
摘要:
为了提高糖尿病前期的检出率，在糖耐量受损（IGT）常规诊断方法的基础上，增加糖化血红蛋白作为糖尿病筛查的因素，构建一个IGT检测模型。采集受试者的身高、体质量、腹围、血压、皮脂厚度、空腹血糖和糖化血红蛋白作为模型的特征输入，用K-近邻算法和神经网络对其分类，模型输出包括血糖值正常、IGT和糖尿病。结果显示增加糖化血红蛋白作为分类特征后，神经网络和K-近邻算法的分类准确率分别为88.89%和93.09%，明显高于传统方法的分类准确率（83.33%和78.38%）。本研究提出的IGT检测模型对糖尿病的临床诊断有重要意义。
Abstract:
Abstract: On the basis of routine methods for diagnosing impaired glucose tolerance (IGT), an IGT detection model is constructed by adding glycosylated hemoglobin as the factor of diabetes screening, thereby improving the detection rate of pre-diabetes. The height, body weight, abdominal circumference, blood pressure, thickness of sebum, fasting blood glucose and glycosylated hemoglobin of subjects were collected as the feature inputs of the model, and then the subjects were classified by K-nearest neighbor algorithm and neural network. The output of the model included normal blood glucose, IGT and diabetes. The results show that after adding glycosylated hemoglobin as the factor of diabetes screening, the classification accuracies of neural network and K-nearest neighbor algorithm are 88.89% and 93.09%, respectively, which are significantly higher than 83.33% and 78.38% of traditional methods. The proposed IGT detection model is of great significance for the clinical diagnosis of diabetes.

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备注/Memo

备注/Memo:
【收稿日期】2020-04-11 【基金项目】国家基金委重大仪器研制项目（61627807）；国家自然科学基金（1873913）；广西自然科学基金（2018GXNSFDA281044）；广西创新研究团队项目（2017GXNSFGA198005） 【作者简介】陈真诚，教授，研究方向：生物传感与智能仪器，E-mail: chenzhcheng@163.com
更新日期/Last Update: 2020-10-29