RESUMEN
Objective] To discuss application of KNN-kernel clustering methods for diarrhea patients serum immune indexes detection data classification and diagnosis of applicability and clinical significance. [Methods] To reveal the applicability and clinical signnificance of KNN-kernel function clustering method in the diagnosis of serun immune index. In this research, the KNNCLUST algorithm is used to program the serum immune index data of 74 patients with diarrhea by Matlab software. [Results] 74 patients were divided into 5 categories by cluster analysis. The patients with diarrhea were divided into rotavirus negative and positive class, and the patients were further subdivided, especially the three early rotavirus tests were negative but later confirmed positive and were clustered into one group. [Conclusions] This can be seen that the KNN-kernel clustering method is helpful for early screening of rotavirus infection, practical clinical significance on the early treatment of disease.