Objective: Because there is more than one hidden layer between the input and output layers in the neural network algorithm, it is called "Deep Neural Networks". In the study, the Deep Neural Networks algorithm; different input (number of layers, epoch, error rate) and evaluation of he performance of the model being practiced an application is intended. Materials and Methods: The most important feature that distinguishes the deep neural network method from the classical neural networks is the number of layers that provide good results in complex problems. art treatment results of patients who used immunotherapy were used as data set in the study. Results: According to this; the simple-layer (1 hidden layer) artificial neural network model was classified with 87.5% overall accuracy and 29.74% MAPE, whereas the deep neural network model was classified with 99.8% overall accuracy and 25.19% MAPE ratio. The study showed that the model of deep neural networks had a higher accuracy rate. Conclusion: As a result of the application performed in this study; it is seen that the multi-layered (deep) neural network model provides classification with higher Accuracy and lower error rate than the single layer (classical) neural network model. In other words, according to the results of this study; it has been found that the deep neural network model has a better (optimum) classification rate than the classical neural network model.
Key Words: Deep neural networks; immunotherapy; multilayer network