| 糖尿病电子病历分类算法性能研究 |
| 修订日期：2017-11-28 点此下载全文 |
| 引用本文：杨美洁,邓媛.糖尿病电子病历分类算法性能研究[J].医学信息学杂志,2018,39(2):65-68,77 |
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| 基金项目:重庆市社会事业与民生保障科技创新专项（项目编号：cstc2015shms-ztzx10003）；重庆医科大学医学信息学院大学生创新实验（项目编号：2015C005）。 |
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| 中文摘要:对糖尿病电子病历的基本信息、出入院记录和病程记录进行数据预处理，利用Weka3.9对处理后的数据分别进行决策树、人工神经网络、朴素贝叶斯和K最近邻分类，结果显示朴素贝叶斯分类法对此类数据的预测和分类更具优势，为糖尿病的分类和预测提供依据。 |
| 中文关键词:SQL 糖尿病 电子病历 Weka 3.9 朴素贝叶斯 |
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| Classification Algorithm Performance Study on Diabetes Electronic Medical Records |
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| Abstract:The paper preprocesses the data including basic information, admission and discharge record and progress note of diabetes Electronic Medical Records(EMR), implementing decision tree, Artificial Neural Network(ANN), Naive bayesian and K-Nearest Neighbor(KNN) classifications respectively on data that have been processed with Weka 3.9. The result shows that Naive bayesian classification, which is superior to the others in predicting and classifying such data, can provide basis for the classification and prediction of diabetes. |
| keywords:SQL Diabetes Electronic Medical Records(EMR) Weka 3.9 Naive bayesian |
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