## 2 Related Work  
The market for mobile devices is growing rapidly and, at the moment, almost every person has a smartphone that can constantly process data from its sensors. At present, a huge number of mobile applications related to medicine are available [4]; however, all of the currently existing applications associated with Parkinson's disease can be divided into three categories:  
* Applications that are designed for physical training; for example, in [5], the created mobile application was used for cognitive stimulation in the elderly. The application offers a number of games for training various cognitive functions (memory, concentration, and so on);
* applications that provide the ability to obtain information about the PD, as a whole, to the user and the ability to obtain information directly from those who have already had experience in solving such problems. Today, there are many websites and online reference books and, therefore, there is no need to download a separate application for a smartphone; and
* applications that, using sensors embedded in a smartphone, allow the user to identify and track certain symptoms. For example, applications for assessing hand tremor [6; 7], and for assessing the symptoms of Parkinson's disease by voice, fine motor skills, and gait [8]. The main disadvantages of all these applications are the lack of communication with a specialist, such as the attending neurologist, and the need to perform certain physical manipulations to assess the symptoms of PD. Many studies use special sensors to assess the patient's condition. These sensors are mounted on the human body. Such studies have been described, for example, in [9; 10; 11]. Unfortunately, such approaches to measuring the parameters of the condition of patients cannot be applied to everyday monitoring. Therefore, we use measuring instruments for hand movements, which are more affordable for many patients. However, it should be noted that, in this case, it is impossible to take into account the mutual correlation between the accelerometers on the arm, trunk, and leg. However, using a phone, it is possible to collect other important parameters about the patient's condition, such as assessing their memory, attention, voice parameters, and emotional state, among others. The next task in creating a patient monitoring system is the development of approaches for intelligent data processing. There are a lot of data and they are very diverse; as such, machine learning methods--in particular, neural networks--are often applied to such data. The comprehensive computational model for the diagnosis of PD based on motor, non-motor, and neuroimaging features using the recently-developed enhanced probabilistic neural network (EPNN) has been previously described in [12], while an artificial neural network system with a back-propagation algorithm for helping doctors in identifying PD has been presented in [9]. A description of the use of two different artificial neural network classifiers--probabilistic neural network (PNN) and classification tree (CIT)--for distinguishing people with Parkinson's disease and people with essential tremor based on 1231-FP-CIT SPECT data has been presented in [13]. A comparison of the effectiveness when using three probabilistic variants of the neural network (PNN)--incremental search (IS), Monte Carlo search (MCS), and hybrid search (HS)--to discriminate between healthy people and people with Parkinson's disease was carried out in [14]. The studies were conducted on a set of biomedical voice measurements obtained from 31 people, 23 having Parkinson's disease (PD). For each person, 195 voice recordings were made. The study was conducted on 22 voice parameters, including the speed of pronunciation of the text, the presence of noise, vocal data, and others. The results showed that there is no significant difference between the three named methods.