基于Logistic回归和多层神经网络的Ⅱ型糖尿病并发症预测
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摘要
研究了Ⅱ型糖尿病并发症的预测。针对相关诊断指标众多,直接应用传统的神经网络等模型预测,会带来无法适应多种并发症、运算速度较慢及预测准确率偏低等问题,提出了基于Logistic回归和多层神经网络(MNN)的Ⅱ型糖尿病并发症预测模型。该模型首先应用关联性分析,提取与5种不同Ⅱ型糖尿病并发症相关的诊断指标,经Logistic回归模型等分析得到强相关因子,作为预测模型的输入,再运用Python,构建基于多层神经网络的预测模型。实验结果表明,全血糖化血红蛋白测定,尿胆原定性实验指标,尿素和尿红细胞与绝大部分Ⅱ型糖尿病并发症直接相关。Logistic回归结合多层神经网络预测准确率高于单一Logistic回归模型,预测准确率基本保持在85%的水平上,对某些并发症的预测准确率达到90%以上,可以达到为Ⅱ型糖尿病并发症预测提供科学参考的目的。
The prediction of type Ⅱ diabetes complications is studied. The diagnostic criteria of type Ⅱ diabetes complications are numerous,and direct application of traditional neural network model prediction will bring about the situation that it cannot adapt to many kinds of complications,slow operation speed and low prediction accuracy. A prediction model for type Ⅱ diabetes complications based on Logistic regression and multilayer neural network( MNN)is proposed. The model firstly uses correlation analysis to extract diagnostic indexes related to five different types of type Ⅱ diabetic complications,and the strong correlation factor is obtained through Logistic regression model analysis,and then a prediction model based on multilayer neural network is constructed by using Python language. The experiment results show that the measurement of total glycemic hemoglobin,urinary biliary original test indicators,urea and urinary red blood cells are related to most of the complications of type Ⅱ diabetes directly. The prediction accuracy of multi-layer neural network is higher than that of Logistic regression model. The prediction accuracies are maintained at 85% basically,and the prediction accuracy of certain complications can reach more than 90%,which provides a scientific reference for the complications of type Ⅱ diabetes.
The prediction of type Ⅱ diabetes complications is studied. The diagnostic criteria of type Ⅱ diabetes complications are numerous,and direct application of traditional neural network model prediction will bring about the situation that it cannot adapt to many kinds of complications,slow operation speed and low prediction accuracy. A prediction model for type Ⅱ diabetes complications based on Logistic regression and multilayer neural network( MNN)is proposed. The model firstly uses correlation analysis to extract diagnostic indexes related to five different types of type Ⅱ diabetic complications,and the strong correlation factor is obtained through Logistic regression model analysis,and then a prediction model based on multilayer neural network is constructed by using Python language. The experiment results show that the measurement of total glycemic hemoglobin,urinary biliary original test indicators,urea and urinary red blood cells are related to most of the complications of type Ⅱ diabetes directly. The prediction accuracy of multi-layer neural network is higher than that of Logistic regression model. The prediction accuracies are maintained at 85% basically,and the prediction accuracy of certain complications can reach more than 90%,which provides a scientific reference for the complications of type Ⅱ diabetes.
引文
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[12]何朝,胡建功,赵莹颖,等.北京市社区高血压患者血压控制影响因素Logistic回归分析[J].现代预防医学,2018,45(7):1211-1215
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[14]袁媛. 2型糖尿病患者糖尿病视网膜病变的预测性因素研究[J].眼科新进展,2015,35(8):784-786
[15]陈渝,宗会娟,李伟. 2型糖尿病危险因素及患病风险预测模型研究[J].昆明理工大学学报(自然科学版),2018,43(2):60-64+70
[2]吕福应,郭国才,黎敦镇,等.糖化血红蛋白对糖尿病诊疗和并发症风险预测的临床价值[J].国际检验医学杂志,2015,36(11):1591-1592
[3]李玲,杨家悦,祝祥云,等.血清胰腺再生蛋白水平预测2型糖尿病及其慢性并发症发生的价值[J].中国全科医学,2016,19(2):159-163
[4]蒙绪标,符兰芳,刘婷婷,等.可溶性内皮细胞蛋白C受体和高敏C反应蛋白水平预测糖尿病患者血管并发症的价值[J].中国现代医学杂志,2016,26(14):58-62
[5]明淑萍,刘玲,周黎,等.糖尿病急性并发症继发轻度认知功能障碍的预测模型及时间窗分析[J].中风与神经疾病杂志,2017,34(9):786-791
[6]李晓燕,孟凡杰,段玉龙,等.改良早期预警评分、血糖值评分及两评分结合预测糖尿病急性并发症患者预后能力的对比研究[J].实用医学杂志,2018,34(3):397-400
[7]张胜,田杰,樊楚,等.基于神经网络的川崎病并发冠状动脉病变预测模型[J].中国生物医学工程学报,2018,37(3):313-318
[8]肖达勇,刘勋,廖骏,等. 2009-2014年重庆市痢疾流行特征及气象因素对其影响的BP神经网络模型研究[J].预防医学情报杂志,2018,34(6):722-727
[9]王斌,冯慧芬,黄平,等.人工神经网络模型在预测重症手足口病中的应用研究[J].现代预防医学,2018,45(11):1921-1924+1947
[10]任红艳,吴伟,李乔玄,等.基于反向传播神经网络模型的广东省登革热疫情预测研究[J].中国媒介生物学及控制杂志,2018,29(3):221-225
[11]徐学琴,杜进林,孙宁,等.改进的BP神经网络模型在麻疹预测中的应用研究[J].中国现代医学杂志,2014,24(31):52-55
[12]何朝,胡建功,赵莹颖,等.北京市社区高血压患者血压控制影响因素Logistic回归分析[J].现代预防医学,2018,45(7):1211-1215
[13]陈德华,洪灵涛,潘乔.基于改进神经网络的糖尿病生化指标值预测[J].微型机与应用,2017,36(5):54-56+59
[14]袁媛. 2型糖尿病患者糖尿病视网膜病变的预测性因素研究[J].眼科新进展,2015,35(8):784-786
[15]陈渝,宗会娟,李伟. 2型糖尿病危险因素及患病风险预测模型研究[J].昆明理工大学学报(自然科学版),2018,43(2):60-64+70
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