ABSTRACT
Based on signal to noise ratio and probabilistic neural network method associated with experimental data,all analysis model in gastric carcinoma is presented.According to the available information,the samples of gastric carcinoma can be tested and ana.Lyzed.The signal to noise ratio is first calculated．Secondly,records in the database are chosen as a training set to build a probabilistie neural network model and the feature subset is selected according to accuracy.Finally,test set is to test accuracy of model．The model is implemented using MATLAB,and it can be generalized and applied to similar disease auxiliary diagnosis region.
ABSTRACT
Based on signal to noise ratio and probabilistic neural network method associated with experimental data,all analysis model in gastric carcinoma is presented.According to the available information,the samples of gastric carcinoma can be tested and ana.Lyzed.The signal to noise ratio is first calculated．Secondly,records in the database are chosen as a training set to build a probabilistie neural network model and the feature subset is selected according to accuracy.Finally,test set is to test accuracy of model．The model is implemented using MATLAB,and it can be generalized and applied to similar disease auxiliary diagnosis region.
ABSTRACT
Based on signal to noise ratio and probabilistic neural network method associated with experimental data,all analysis model in gastric carcinoma is presented.According to the available information,the samples of gastric carcinoma can be tested and ana.Lyzed.The signal to noise ratio is first calculated．Secondly,records in the database are chosen as a training set to build a probabilistie neural network model and the feature subset is selected according to accuracy.Finally,test set is to test accuracy of model．The model is implemented using MATLAB,and it can be generalized and applied to similar disease auxiliary diagnosis region.