###### Abstract  
This article describes an approach for collecting and pre-processing phone owner data, including their voice, in order to classify their condition using data mining methods. The most important research results presented in this article are the developed approaches for the processing of patient voices and the use of genetic algorithms to select the architecture of the neural network in the monitoring system for patients with Parkinson's disease. The process used to pre-process a person's voice is described in order to determine the main parameters that can be used in assessing a person's condition. It is shown that the efficiency of using genetic algorithms for constructing neural networks depends on the composition of the data. As a result, the best result in the accuracy of assessing the patient's condition can be obtained by a hybrid approach, where a part of the neural network architecture is selected analytically manually, while the other part is built automatically.  
[PERSON]'s disease; voice processing; mobile devices; patient monitoring; neural networks; genetic algorithms +
Footnote †: journal: Medical  
\(\bigcirc\)  loss of diary, and so on.  
Use of the capabilities of mobile phones can help to solve these monitoring problems.  
A mobile phone is a personal device that is almost always near the user. Therefore, it is logical to use mobile phones to collect data that can help doctors diagnose the improvement or deterioration in a patient's condition. With the help of mobile phones, the patient can describe their health condition, take notes for the doctor, and so on. In addition, some of the work related to collecting and processing data can be transferred from the patient and the doctor using mobile devices. A mobile phone can contain up to 14 different sensors [3], which allow for tracking changes in coordinates, atmospheric pressure, air temperature, and so on.  
One of the most important sources of information about a person's condition is their voice. People's voices differ from each other in many parameters. Voice sounds are characterized by strength, timbre, pitch, and other characteristics. Changing a person's voice in various diseases and in the course of diseases is also an individual property of a person.  
At present, the intensive development of technologies related to audio information is well underway. However, the search for and development of methods to automate the processing of audio information is still an urgent problem. The solving of these problems is important for both existing and new tasks in various fields of human activity.  
This article presents the results of research in two areas: the development of approaches to processing voice data and the construction of neural networks for classifying the condition of patients with Parkinson's disease.  
The article is structured as follows: Section 2 presents an overview of existing solutions for monitoring PD symptoms using mobile devices, an overview of methods for processing voice data, and solutions for the use of neural networks in medicine. Section 3 is devoted to the description of our purpose and objectives. Section 4 provides a brief description of the data collection system. Section 5 describes an approach making use of genetic algorithms for constructing neural networks. Section 6 discusses approaches to processing a person's voice and describes other data obtained about the patient's condition from a mobile phone. Section 7 gives the results of the experiment. Finally, the conclusion summarizes the research results and briefly describes further steps for developing a system for monitoring the condition of patients with PD.