失稳网络医保信息欺诈检测算法研究

传统医保信息欺诈检测算法存在运行时间长、效率低的问题，无法保障患者医保信息安全，为了解决该问题，采用基于随机森林算法对失稳网络医保信息欺诈行为进行检测。通过混合抽样可抽取在失稳情况下的数据，并建立非平衡数据分类算法抽样机制；进行迭代随机森林数据计算，采用多数投票法构建基分类器，并以此为基础筛选异常数据；利用模型实现该算法对医保信息欺诈检测。设计对比实验，验证该算法有效性。通过实验结果可知，基于随机森林算法运行时间较短、效率高。

Research on information fraud detection algorithm for unstable network medical insurance

Traditional health insurance information fraud detection algorithm has many problems such as long running time and low efficiency, which can not guarantee the safety of medical insurance information. In order to solve this problem, we use random forest algorithm to detect medical fraud information in unstable network. Through extracting mixed sampling instability in the case of data, and the establishment of the imbalanced data classification algorithm for iterative sampling mechanism; random forest data, builds a classifier using voting method, and on the basis of screening of abnormal data; the algorithm of insurance fraud detection using information model. Design comparison experiment to verify the effectiveness of the algorithm. The experimental results show that the run time based on random forest algorithm is shorter and more efficient.