Application of radial basis function neural network for grading of gliomas / 生物医学工程学杂志

ABSTRACT

This retrospective investigation was directed to the applicability of Radial Basis Function Neural Network (RBF-NN) and Discriminant Analysis in the grading of gliomas. The data on 116 patients with primary glioma in our hospital from February 2008 to April 2009 were collected. Kruskal-Wallis H test was used to draw in the variable age ranks and then to take them out from the range of different grades of gliomas. The results of RBF-NN model, discriminant analysis, and the combined model of RBF-NN and discriminant analysis were evaluated and compared respectively with and without age. In this study, different classifications of gliomas showed statistically significant differences in age and the accuracy of the models with age was better than the ones without age. The predictive accuracy and Kappa value of RBF-NN model and the combined model were also better than those exhibited by Bayes discriminant analysis. Consequently, as a prediction model, or to help other models, RBF-NN is of significance to predicting the grade of gliomas.