糖尿病电子病历分类算法性能研究

Classification Algorithm Performance Study on Diabetes Electronic Medical Records

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。

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.