改进模糊聚类在医疗卫生数据的Takagi-Sugeno模糊模型

Takagi-Sugeno fuzzy modeling based on improved fuzzy clustering for health care data

传统的数据分析方法在挖掘医学数据信息时,没有充分利用可用的信息.针对这一问题,提出一种基于改进模糊聚类的Takagi-Sugeno(T-S)模糊系统,将系数调节与指数调节与经典模糊C均值聚类(Fuzzy C-means,FCM)算法结合,替换经典T-S模糊系统中的逻辑元件,合理利用T-S模糊系统在预测与回归等方面的优势的同时,通过指数或系数的灵活调控,深度挖掘医学数据中不同属性间的关联信息,提高算法在众多医学数据分析预测中的准确性.为具体评估算法有效性,在真实医疗数据集上进行实验,实验结果表明,该算法具有更高的预测精度及可行性.

A novel Takagi-Sugeno(T-S) fuzzy system based on an improved fuzzy clustering algorithm is developed to fully leverage useful information in medical data. By combining the classical Fuzzy C-Means(FCM) with adaptive parameters in distance measures and simultaneously keeping the basic structure of T-S fuzzy systems,the proposed fuzzy system has its adaptive modeling superiority in prediction for medical data. The experimental results on the adopted medical datasets indicate both promising performance and feasibility of the proposed fuzzy system.