## 6 Description of Test Data
It can be concluded that, for each user, these parameters will differ from other users and, as such, it will be possible to determine the similarity of intonations. Testing a Model for Assessing the Condition of a Patient with Parkinson's Disease Based on a Neural Network and a Genetic Algorithm  
To assess the patient's condition, we built several neural networks. The general diagram of the patient's condition analysis system is shown in Figure 1.  
The original neural network architecture for rotation angles and voice parameters is shown in Figure 2.  
The neural network consisted of five layers: The first and second are recurrent LSTMs with 128 neurons, followed by fully connected layers with 64 and 32 neurons, and one output neuron. The introduction of the second recurrent layer made it possible to increase the learning rate and the accuracy of the neural network by reducing the time required for calculating the results of PD severity based on the data provided. The parameters obtained during the execution of the test modules, such as the speed of movement, the accuracy of the entered text, the accuracy of hitting visual elements, the assessment of fine motor skills, and the reproduction of a geometric figure, required building a separate neural network. The first layer is a recurrent LSTM consisting of 150 neurons. The subsequent layers are fully connected, including 256, 128, 64, 16, and one neuron. A Dropout layer was introduced between the fully connected layers, as the neural network model only explained examples from the training set, instead of learning to classify examples from the test set. Dropout, or the \"thinning method\", passes through all neurons of a certain layer and, with probability \(p\), completely excludes them from the network for the duration of the iteration. Thus, the network relies on a \"consensus opinion,\" rather than the opinion of a particular neuron. The neural network architecture for test modules is shown in Figure 3.  
Figure 1: General architecture of a system of neural networks for PD analysis. The number of neurons for all architectures was selected by a heuristic method, where the following parameters were used for construction:  
* Binary cross-entropy (BCE) was used as a loss function;
* The standard deviation (MSE) was used as a metric for learning quality;
* The activation function ReLU was used in the hidden layers of the neural network, while that in the output layer was Sigmoid. Figure 3: Architecture of the neural network for testing modules. Figure 2: Neural network architecture for assessing changes in the values of the angles of rotation, tilt of a mobile phone, and voice parameters. As a result of application of the genetic algorithm, several neural network architectures were built. The results of training are presented in Figure 4. On the abscissa axis is the ordinal number of the training epoch, while the binary cross-entropy value is presented on the ordinate axis, which determines the value of the loss of the neural network computation result from the corresponding value in the validation sample. The blue curve denotes the loss function for the training sample, the orange one for the test sample. Figure 4 shows graphs of loss functions when training a neural network using test data for only two parameters: the angles of rotation and tilt of a mobile device  
The training and testing errors decreased, but the value of the testing error for a given architecture of a neural network designed by an analytical method was less than that for a neural network built using a genetic algorithm (i.e., it summarizes data better). Comparative characteristics of the architectures of these networks are presented in Table 3.  
The accuracy rates for an architecture designed without the use of a genetic algorithm were significantly superior to those of a neural network generated by an analytical method.