基于KNN核函数聚类方法在医学指标分类诊断中的应用

Application of Clustering Method Based on KNN Kernel Function in Medical Index Classification Diagnosis

目的:探讨基于KNN(K-Nearest Neighbors,KNN)核函数聚类方法在乙肝病毒分类诊断中的临床医学意义和其他疾病诊断的适用性。方法:将收集来自于医院的93例乙肝患病采用基于KNN核函数分类算法进行聚类,通过SPSS数据初处理和MATLAB编程实现。揭示基于K近邻核函数在乙肝病毒分类诊断中有重大的意义。结果:通过核函数聚类方法将93例乙肝患者聚成四类,不仅划分出急性乙肝和慢性乙肝,而且发现HBC IGM也是划分急慢性乙肝的一个不可缺少的参数。结论:基于K近邻核函数的分类方法,在诊断对急慢性乙肝疾病划分有一定帮助,对后期实现计算机辅助分类诊断具有一定的意义。

Objective:To explore the clinical significance of KNN(K-Nearest Neighbors,KNN)clustering method in the diagnosis of hepatitis B virus classification and the applicability of other disease diagnosis.Methods:The collection of 93 patients with hepatitis B from the hospital was clustered based on KNN kernel function classification algorithm.The data was processed by SPSS data processing and MATLAB programming.It is of great significance to reveal the classification diagnosis of hepatitis B virus based on K-nearest neighbor kernel function.Results:93 cases of hepatitis B patients were clustered into four groups by kernel function clustering method.Not only acute hepatitis B and chronic hepatitis B were classified,but also HBC IGM was found to be an indispensable parameter for the classification of acute and chronic hepatitis B.Conclusion:The classification method based on the K-nearest neighbor kernel function is helpful in the diagnosis of acute and chronic hepatitis B disease,and it has certain significance for the later computer-assisted classification diagnosis.