微型电脑应用

随着病人数量的不断增长，医院信息系统中的数据也在不断膨胀，对其进行有效挖掘是医生有效诊断的关键。基于此，通过提出改进的按照时间排列文件的起泡排序(FsO)算法，并在实际分类中融合模糊决策树(FDT),得到IFsO-FDT,实际验证其性能和有效性。实验结果表明，IFsO-FDT与其他分类模型比较中表现出更好的性能，准确率最高达到了98.14%,在4个数据集中都表现良好。其更为适应乳腺癌数据集，分类准确度均超过了90%,适应能力和概括能力都是最高的，分别为0.80和1.03。综合来看，IFsO-FDT具备更高的分类精度，在实际医院信息系统数据分类中具备重大的应用价值。

With the growing number of patients, the data in the hospital information system are also expanding, and effective data mining is the key to the effective diagnosis of doctors. Based on this, IFsO-FDT is obtained by proposing an improved bubble sort files by time(FsO) algorithm and fusing fuzzy decision tree(FDT) in the actual classification, and its performance and effectiveness are verified in practice. The experimental results show that the IFsO-FDT has better performance compared with other classification models, with the highest accuracy of 98.14%. It performs well in four data sets, among which it is more suitable for breast cancer data sets, with classification accuracy of more than 90%. The adaptability and generalization ability are the highest which are 0.80 and 1.03. In a word, IFsO-FDT has higher classification accuracy, and has great application value in actual hospital information system data classification.