基于线性判别式和支撑向量机的肾结石分类方法

The classification of kidney stones based on support vector machine and linear discriminant analysis

用支撑向量机(SVM)方法辅助诊断肾结石,并和线性判别式方法作比较,结果显示这两种方法都表现出了很好的预测能力．鉴于 SVM 是用于解决非线性的良好方法．因此,SVM 作为一种有效的机器学习方法是可以用来进行肾结石的辅助诊断和分类研究的．肾结石的成因比较复杂,与自然环境、社会生活条件、全身性代谢紊乱及泌尿系统本身的疾患有关,本文从钙离子生物学特性方面讨论了钙盐结石的成因．

The support vector machine（SVM） is, for the first time, used to diagnose kidney stones and compared with linear discriminant analysis. According to results of the two methods, they both show good prediction ability, indicating that SVM is an effective tool for the classification of kidney stones. The formation of kidney stone is connected with the environment, living conditions, bodily disorder and urinary diseases. This paper discusses the formation of kidney stone from the characters of calcium ion.