On the quality of training artificial neural networks without initial data preprocessing under conditions of their limited set

The task of training artificial neural networks (ANNs) for the diagnosis of Alzheimer's disease based on the magnetic susceptibility of 10 brain veins under conditions of limited initial data (81 patients) is considered. In a recent study, fully-connected ANNs with one and two hidden layers were successfully trained in a recent study after reducing the number of veins to 4 and determining the number of synaptic connections comparable to a neural network based on a decision root. The known results gave rise to the hypothesis that training ANNs on the same data without preprocessing would also yield high-quality neural networks. To assess the quality of ANNs, the following indicators were used: mean square errors of training and generalization (testing), coefficients of determination (R2) on the sets of values used in training and generalization (testing), as well as the number of trained ANNs satisfying a certain value of the above indicators. For graphical representation of the results of repeated experiments on training and testing of ANNs, a new graph is proposed to visualize the quality of ANNs. Thus, the paper demonstrates the quality indicators of 800 ANNs trained without data preprocessing, among which there were 7 neural networks with one hidden layer and 9 neural networks with two hidden layers, for each of which 50 experiments were conducted. For comparison, we present the quality indices of 2400 ANNs trained with preprocessing.