《中国康复理论与实践》

《中国康复理论与实践》 ›› 2009, Vol. 15 ›› Issue (11): 1086-1088.

• 康复工程 • 上一篇    下一篇

粗糙集和支持向量机应用于帕金森病辅助诊断

王安睿;费树岷   

  1. 东南大学自动化学院,江苏南京市 210096
  • 收稿日期:2009-09-10 出版日期:2009-11-01 发布日期:2009-11-01

Rough Set and Support Vector Machines for Assistant Detection of Parkinson Disease

WANG An-rui，FEI Shu-min   

  1. School of Automation, Southeast University, Nanjing 210096, Jiangsu, China
  • Received:2009-09-10 Published:2009-11-01 Online:2009-11-01

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摘要： 目的 研究基于粗糙集和支持向量机辅助诊断帕金森病的可行性。方法 利用粗糙集理论中基于属性重要度的约简算法，对临床诊断帕金森病的常用特征进行约简，再分别用基于线性、多项式和径向基（RBF）核函数的支持向量机实现分类，与传统BP神经网络分类结果比较。结果 属性约简与支持向量机结合的算法预测准确率为92.71%，比传统BP神经网络算法在准确率和稳定性方面都有优势。结论 粗糙集和支持向量机结合的方法可以提高分类的准确率，节省资源，是临床上辅助诊断帕金森病的一个有效手段。

关键词: 粗糙集, 属性约简, 支持向量机, 帕金森病

Abstract: Objective To study the feasibility of using rough set and support vector machines to detect Parkinson disease. Methods The reduction algorithm based on the importance of the attributes in the rough set theory was used to reduce the common diagnosis features in the clinical practice. The support vector machines were applied for classification with the linear, polynomial and RBF kernel, and the Results were compare with that of BP neural network. Results The algorism combined attributes reduction and support vector machines appeared the highest accuracy of 92.71% in the classification, which seemed greater advantage in accuracy and stability than BP neural network. Conclusion Improving the accuracy of the classification as well as saving the resources, rough set and support vector machines are proved to be an effective method to assist the clinical diagnosis of Parkinson disease.

Key words: rough set, attributes reduction, support vector machines, Parkinson disease