The DAGs-MLP Structure to the Efficiency of Neural Network Classification for Diagnosis of Hepatobiliary Disoders

We present the effect of directed acyclic graph (DAGs) to the efficiency of neural network models in a diagnosis of hepatobiliary disorder. We compare the model of DAGs structure in the general neural network model with the general neural network model. We name these new algorithms as DAGs-MLP, DAGs-SVM, DAGs-RBF and DAGs-RPI. The efficiency of each algorithm from the two models is determined from the accuracy of data classification. The results show that DAG methods can improve the efficiency of general algorithms focused in this research. DAGs-MLP technique has the best efficiency