摘要
糖尿病的早期发现,对成功控制、预防并发症,降低患病率具有重要意义.现有基于机器学习建立的糖尿病诊断模型,由于泛化能力不足而导致精度较低.为此,本文提出结合批归一化的多层感知机模型,保证模型中数据分布的一致性.基于PIMA数据集进行训练评估,实验结果表明该模型用于糖尿病早期识别泛化能力好、收敛速度快且有较高的准确率.
The early detection of diabetes is of great significance for successful control of diabetes,prevention of complications,and reduction of prevalence.Existing diabetes diagnosis models based on machine learning have weak precision due to insufficient generalization ability.Therefore,this study proposes a multi-layer perceptron model combined with batch normalization to ensure the consistency of data distribution in the model.The proposed model is based on the PIMA training set for training evaluation.The experimental results show that the model has sound generalization ability in early recognition of diabetes,fast convergence,and high accuracy.
作者
胡清礼
胡建强
余小燕
HU Qing-Li;HU Jian-Qiang;YU Xiao-Yan(School of Computer and Information Engineering,Xiamen University of Technology,Xiamen 361024,China;School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China)
出处
《计算机系统应用》
2020年第5期182-188,共7页
Computer Systems & Applications
基金
福建省自然科学基金(2019J01856)
赛尔网络下一代互联网创新项目(NGII20160708)。
关键词
糖尿病
机器学习
批归一化
泛化能力
diabetes
machine learning
batch normalization
generalization ability
