结合改进PSO和模糊决策树的医院信息系统数据分类研究  

Research on Data Classification of Hospital Information System by Combining Improved PSO and Fuzzy Decision Tree

作　　者：李梦 LI Meng(Second Hospital of Zhangjiakou City,Zhangjiakou 075000,China)

机构地区：[1]张家口市第二医院,河北张家口075000

出　　处：《微型电脑应用》2024年第9期194-196,201,共4页Microcomputer Applications

摘　　要：医疗系统的使用量与日俱增导致医疗系统中的相关医疗数据也在不断地增加。为此优化模糊ID3算法和改进粒子群算法,并将改进粒子群算法进行智能寻优从而改善模糊决策树性能。实验结果表明,提出的IPSO-FDT算法在皮马印第安人糖尿病、威斯康星乳腺癌和帕金森氏病数据集中的测试准确率分别为97.9%、89.5%和81.8%,在生成树的概括能力数值分别为1.003、1.034、1.026,显著高于比较模型。实验数据证明,在医院信息系统进行数据分类的多种算法中,此设计的结合改进PSO与模糊决策树算法具有一定适用性。The use of the medical system is increasing day by day,and leads to the increase of relevant medical data in the medical system.This research optimizes the fuzzy ID3 algorithm and improves the particle swarm algorithm,and uses the improved particle swarm algorithm to enhance the function of fuzzy decision tree.According to the experimental results that the test accuracy rates of the IPSO-FDT algorithm proposed in this study in the Pima Indians diabetes,Wisconsin breast cancer and Parkinson’s disease dataset are 97.9%,89.5%and 81.8%.The generalization ability values of the spanning tree are 1.003,1.034,1.026,which are significantly higher than the comparison model.The experimental data proves that the design combined with improved PSO and fuzzy decision tree has certain applicability in the field of hospital information system data classification.

关 键 词：模糊决策树 医院信息 数据分类 改进粒子群算法 

分 类 号：R197[医药卫生—卫生事业管理]