基于数据挖掘技术的医疗数据信息提取仿真

Medical Data InformationExtraction Simulation Based on Data Mining Technology

完整、快速且准确提取医疗数据信息可以为医疗提供有效辅助。针对传统医疗数据信息提取时出现的提取效率低以及提取结果不完整的问题,提出一种基于数据挖掘技术的医疗数据信息提取方法。首先通过先验算法与FP-Growth算法构建信息关联规则,确定医疗数据内各项间的内在关联;然后使用遗传算法约简数据信息,保证处理后获取的约简数量较小,但不可为零,获取约间分类属性集合。最后根据医疗分类诊断中C4.5决策树与ID3算法的运作过程,构建决策树模型,计算医疗数据信息所有属性的信息增益几率,实现完整、准确提取医疗数据信息的目的。仿真证明,所提方法在提取医疗数据信息时有着效率快、完整性高的优点。

Complete, fast and accurate extraction for medical data information can provide effective assistance for medical treatment. In this article, a method of extracting medical data information based on data mining technology was presented. Firstly, the information association rule was constructed by the prior algorithm and FP-Growth algorithm, and then the internal association among the items in medical data was determined. Secondly, the genetic algorithm was used to reduce data information, so as to ensure that the amount of reduced data information was small, but not zero. Moreover, the set of classification attributes was obtained. According to the operation process of C4.5 decision tree and ID3 algorithm in medical classification diagnosis, the decision tree model was constructed to calculate the probability of information gain of all attributes of medical data information, and thus to achieve complete and accurate extraction for medical data information. Simulation results prove that the proposed method has the advantages of high efficiency and integrity in extracting medical data information.