基于决策树算法的医疗大数据填补及分类仿真

从大数据的基本特点和医疗大数据研究现状出发,分析处理过程中存在的问题,提出在决策树算法下的医疗大数据填补及分类方法。分析医疗数据的关联规则,采用关联分析(Apriori)算法和频繁模式树(Frequent Pattern Growth,FP-Growth)算法挖掘数据。以挖掘数据为基础填补其中的缺失数据,按照医疗数据特点搭建决策树,并运用ID3和C4.5决策树算法,实现医疗大数据的分类,得出数据分类结果。由仿真得出,与传统方法相比,填补量提高了50%,分类精度提高了11.40%、14.80%,无论从数据的填补方面还是分类方法,上述方法均有较高的应用价值,为医疗大数据体系的构建开辟了新的思路。

Medical Big Data Filling and Classification Simulation Based on Decision Tree Algorithm

Based on the basic feature of medical big data,this paper analyzed the problems in data processing and proposed a method of medical big data filling and classification based on the decision tree algorithm.Firstly,the association rule of medical data was analyzed,and then Apriori algorithm and Frequent Pattern Growth(FP-growth)algorithm were adopted to mine data.The mining data was used to fill the missing data.Moreover,a decision tree was built according to the characteristics of medical data.Finally,ID3&C4.5 decision tree algorithm was adopted to realize the classification of medical big data,and thus to obtain the classification results.Simulation results show that,compared with the traditional method,the proposed method increases the amount of data filling amount by 50%,and improves the classification accuracy by 11.40% and 14.80%.No matter from the data filling or the classification method,the proposed method has higher application value.Therefore,this method provides new ideas for the construction of medical big data system.