38 research outputs found
HCT: Hybrid Convnet-Transformer for Parkinson's disease detection and severity prediction from gait
In this paper, we propose a novel deep learning method based on a new Hybrid
ConvNet-Transformer architecture to detect and stage Parkinson's disease (PD)
from gait data. We adopt a two-step approach by dividing the problem into two
sub-problems. Our Hybrid ConvNet-Transformer model first distinguishes healthy
versus parkinsonian patients. If the patient is parkinsonian, a multi-class
Hybrid ConvNet-Transformer model determines the Hoehn and Yahr (H&Y) score to
assess the PD severity stage. Our hybrid architecture exploits the strengths of
both Convolutional Neural Networks (ConvNets) and Transformers to accurately
detect PD and determine the severity stage. In particular, we take advantage of
ConvNets to capture local patterns and correlations in the data, while we
exploit Transformers for handling long-term dependencies in the input signal.
We show that our hybrid method achieves superior performance when compared to
other state-of-the-art methods, with a PD detection accuracy of 97% and a
severity staging accuracy of 87%. Our source code is available at:
https://github.com/SafwenNaimiComment: 6 pages, 6 figures, 3 tables, Accepted for publication in IEEE
International Conference on Machine Learning and Applications (ICMLA),
copyright IEE
Transformers for 1D Signals in Parkinson's Disease Detection from Gait
This paper focuses on the detection of Parkinson's disease based on the
analysis of a patient's gait. The growing popularity and success of Transformer
networks in natural language processing and image recognition motivated us to
develop a novel method for this problem based on an automatic features
extraction via Transformers. The use of Transformers in 1D signal is not really
widespread yet, but we show in this paper that they are effective in extracting
relevant features from 1D signals. As Transformers require a lot of memory, we
decoupled temporal and spatial information to make the model smaller. Our
architecture used temporal Transformers, dimension reduction layers to reduce
the dimension of the data, a spatial Transformer, two fully connected layers
and an output layer for the final prediction. Our model outperforms the current
state-of-the-art algorithm with 95.2\% accuracy in distinguishing a
Parkinsonian patient from a healthy one on the Physionet dataset. A key
learning from this work is that Transformers allow for greater stability in
results. The source code and pre-trained models are released in
https://github.com/DucMinhDimitriNguyen/Transformers-for-1D-signals-in-Parkinson-s-disease-detection-from-gait.gitComment: International Conference on Pattern Recognition (ICPR 2022
Determining the severity of Parkinsonβs disease in patients using a multi task neural network
[EN] Parkinsonβs disease is easy to diagnose when it is advanced, but it is very difficult to diagnose in its early stages. Early diagnosis is essential to be able to treat the symptoms. It impacts on daily activities and reduces the quality of life of both the patients and their families and it is also the second most prevalent neurodegenerative disorder after Alzheimer in people over the age of 60. Most current studies on the prediction of Parkinsonβs severity are carried out in advanced stages of the disease. In this work, the study analyzes a set of variables that can be easily extracted from voice analysis, making it a very non-intrusive technique. In this paper, a method based on different deep learning techniques is proposed with two purposes. On the one hand, to find out if a person has severe or non-severe Parkinsonβs disease, and on the other hand, to determine by means of regression techniques the degree of evolution of the disease in a given patient. The UPDRS (Unified Parkinsonβs Disease Rating Scale) has been used by taking into account both the motor and total labels, and the best results have been obtained using a mixed multi-layer perceptron (MLP) that classifies and regresses at the same time and the most important features of the data obtained are taken as input, using an autoencoder. A success rate of 99.15% has been achieved in the problem of predicting whether a person suffers from severe Parkinsonβs disease or non-severe Parkinsonβs disease. In the degree of disease involvement prediction problem case, a MSE (Mean Squared Error) of 0.15 has been obtained. Using a full deep learning pipeline for data preprocessing and classification has proven to be very promising in the field Parkinsonβs outperforming the state-of-the-art proposals.SIPublicaciΓ³n en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y LeΓ³n (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEΓN, ActuaciΓ³n:20007-CL - Apoyo Consorcio BUCL
It diagnostics of parkinson's disease based on voice markers and decreased motor activity
The objectives of the article to propose the method for complex recognition of Parkinson's disease using machine learning, based on markers of voice analysis and changes in patient movements on known data sets. The time-frequency function, (the wavelet function) and the Meyer kepstral coefficient function are used. The KNN algorithm and the algorithm of a two-layer neural network were used for training and testing on publicly available datasets on speech changes and motion retardation in Parkinson's disease. A Bayesian optimizer was also used to improve the hyperparameters of the KNN algorithm. The constructed models achieved an accuracy of 94.7 % and 96.2 % on a data set on speech changes in patients with Parkinson's disease and a data set on slowing down the movement of patients, respectively. The recognition results are close to the world level. The proposed technique is intended for use in the subsystem of IT diagnostics of nervous diseases
Modified SqueezeNet Architecture for Parkinson's Disease Detection Based on Keypress Data
Parkinsonβs disease (PD) is the most common form of Parkinsonism, which is a group of neurological disorders with PD-like motor impairments. The disease affects over 6 million people worldwide and is characterized by motor and non-motor symptoms. The affected person has trouble in controlling movements, which may affect simple daily-life tasks, such as typing on a computer. We propose the application of a modified SqueezeNet convolutional neural network (CNN) for detecting PD based on the subjectβs key-typing patterns. First, the data are pre-processed using data standardization and the Synthetic Minority Oversampling Technique (SMOTE), and then a Continuous Wavelet Transformation is applied to generate spectrograms used for training and testing a modified SqueezeNet model. The modified SqueezeNet model achieved an accuracy of 90%, representing a noticeable improvement in comparison to other approaches
Π Π°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΠ΅ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π±ΠΎΠ»Π΅Π·Π½ΠΈ ΠΠ°ΡΠΊΠΈΠ½ΡΠΎΠ½Π° Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π°Π½Π°Π»ΠΈΠ·Π° Π³ΠΎΠ»ΠΎΡΠΎΠ²ΡΡ ΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ² ΠΈ Π΄Π²ΠΈΠ³Π°ΡΠ΅Π»ΡΠ½ΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ
Objectives. The problem of IT diagnostics of signs of Parkinson's disease is solved by analyzing changes in the voice and slowing down the movement of patients. The urgency of the task is associated with the need for early diagnosis of the disease. A method of complex recognition of Parkinson's disease using machine learning is proposed, based on markers of voice analysis and changes in the patient's movements on known data sets.Methods. The time-frequency function (the wavelet function) and the Meyer kepstral coefficient function, the KNN algorithm (k-Nearest Neighbors, KNN) and the algorithm of a two-layer neural network are used for training and testing on publicly available datasets on speech changes and motion retardation in Parkinson's disease. A Bayesian optimizer is also used to improve the hyperparameters of the KNN algorithm.Results. The KNN algorithm was used for speech recognition of patients, the test accuracy of 94.7% was achieved in the diagnosis of Parkinson's disease by voice change. The Bayesian neural network algorithm was applied to recognize the slowing down of the patients' movements, it gave a test accuracy of 96.2% for the diagnosis of Parkinson's disease.Conclusion. The obtained results of recognition of signs of Parkinson's disease are close to the world level. On the same set of data on speech changes of patients, one of the best indicators of foreign studies is 95.8%. On the same set of data on motion deceleration, one of the best indicators of foreign researchers is 98.8%. The proposed author's technique is intended for use in the subsystem of IT diagnostics of neurological diseases of a Smart city.Π¦Π΅Π»ΠΈ. Π Π΅ΡΠ°Π΅ΡΡΡ Π·Π°Π΄Π°ΡΠ° ΠΠ’-Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π±ΠΎΠ»Π΅Π·Π½ΠΈ ΠΠ°ΡΠΊΠΈΠ½ΡΠΎΠ½Π° ΠΏΠΎ Π°Π½Π°Π»ΠΈΠ·Ρ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ Π³ΠΎΠ»ΠΎΡΠ° ΠΈ Π·Π°ΠΌΠ΅Π΄Π»Π΅Π½ΠΈΡ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ². ΠΠΊΡΡΠ°Π»ΡΠ½ΠΎΡΡΡ Π·Π°Π΄Π°ΡΠΈ ΡΠ²ΡΠ·Π°Π½Π° Ρ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡΡ ΡΠ°Π½Π½Π΅ΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡ. ΠΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ ΠΌΠ΅ΡΠΎΠ΄ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ³ΠΎ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ Π±ΠΎΠ»Π΅Π·Π½ΠΈ ΠΠ°ΡΠΊΠΈΠ½ΡΠΎΠ½Π° Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΠΉ Π½Π° Π°Π½Π°Π»ΠΈΠ·Π΅ Π³ΠΎΠ»ΠΎΡΠΎΠ²ΡΡ
ΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ² ΠΈ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ Π² Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡΡ
ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Π½Π° ΠΈΠ·Π²Π΅ΡΡΠ½ΡΡ
Π½Π°Π±ΠΎΡΠ°Ρ
Π΄Π°Π½Π½ΡΡ
.ΠΠ΅ΡΠΎΠ΄Ρ. ΠΡΠΏΠΎΠ»ΡΠ·ΡΡΡΡΡ ΡΠ°ΡΡΠΎΡΠ½ΠΎ-Π²ΡΠ΅ΠΌΠ΅Π½Π½Π°Ρ ΡΡΠ½ΠΊΡΠΈΡ (ΡΡΠ½ΠΊΡΠΈΡ Π²Π΅ΠΉΠ²Π»Π΅ΡΠ°), ΡΡΠ½ΠΊΡΠΈΡ ΠΊΠ΅ΠΏΡΡΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΠ° ΠΠ΅ΠΉΠ΅ΡΠ°, Π°Π»Π³ΠΎΡΠΈΡΠΌ k-Π±Π»ΠΈΠΆΠ°ΠΉΡΠΈΡ
ΡΠΎΡΠ΅Π΄Π΅ΠΉ (k-Nearest Neighbors, KNN), Π°Π»Π³ΠΎΡΠΈΡΠΌ Π΄Π²ΡΡ
ΡΠ»ΠΎΠΉΠ½ΠΎΠΉ Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΠΉ ΡΠ΅ΡΠΈ Π΄Π»Ρ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ ΠΈ ΡΠ΅ΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π½Π° ΠΎΠ±ΡΠ΅Π΄ΠΎΡΡΡΠΏΠ½ΡΡ
Π½Π°Π±ΠΎΡΠ°Ρ
Π΄Π°Π½Π½ΡΡ
ΠΏΠΎ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΠ΅ΡΠΈ ΠΈ Π·Π°ΠΌΠ΅Π΄Π»Π΅Π½ΠΈΡ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ ΠΏΡΠΈ Π±ΠΎΠ»Π΅Π·Π½ΠΈ ΠΠ°ΡΠΊΠΈΠ½ΡΠΎΠ½Π°, Π° ΡΠ°ΠΊΠΆΠ΅ Π±Π°ΠΉΠ΅ΡΠΎΠ²ΡΠΊΠΈΠΉ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΎΡ Π΄Π»Ρ ΡΠ»ΡΡΡΠ΅Π½ΠΈΡ Π³ΠΈΠΏΠ΅ΡΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° KNN.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. ΠΠ»Π³ΠΎΡΠΈΡΠΌ KNN ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ Π΄Π»Ρ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ ΡΠ΅ΡΠΈ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ², ΡΠΎΡΠ½ΠΎΡΡΡ ΡΠ΅ΡΡΠ° 94,7 % Π΄ΠΎΡΡΠΈΠ³Π½ΡΡΠ° ΠΏΡΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ΅ Π±ΠΎΠ»Π΅Π·Π½ΠΈ ΠΠ°ΡΠΊΠΈΠ½ΡΠΎΠ½Π° ΠΏΠΎ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ Π³ΠΎΠ»ΠΎΡΠ°. ΠΠ»Π³ΠΎΡΠΈΡΠΌ Π±Π°ΠΉΠ΅ΡΠΎΠ²ΡΠΊΠΎΠΉ Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΠΉ ΡΠ΅ΡΠΈ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ Π΄Π»Ρ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ Π·Π°ΠΌΠ΅Π΄Π»Π΅Π½ΠΈΡ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ², ΠΎΠ½ Π΄Π°Π» ΡΠΎΡΠ½ΠΎΡΡΡ ΡΠ΅ΡΡΠ° 96,2 %.ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅. ΠΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π±ΠΎΠ»Π΅Π·Π½ΠΈ ΠΠ°ΡΠΊΠΈΠ½ΡΠΎΠ½Π° Π±Π»ΠΈΠ·ΠΊΠΈ ΠΊ ΠΌΠΈΡΠΎΠ²ΠΎΠΌΡ ΡΡΠΎΠ²Π½Ρ. ΠΠ° ΡΠΎΠΌ ΠΆΠ΅ Π½Π°Π±ΠΎΡΠ΅ Π΄Π°Π½Π½ΡΡ
ΠΏΠΎ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΠ΅ΡΠΈ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² ΠΎΠ΄ΠΈΠ½ ΠΈΠ· Π»ΡΡΡΠΈΡ
ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ Π·Π°ΡΡΠ±Π΅ΠΆΠ½ΡΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ ΡΠΎΡΡΠ°Π²Π»ΡΠ΅Ρ 95,8 %, Π° Π½Π° Π½Π°Π±ΠΎΡΠ΅ Π΄Π°Π½Π½ΡΡ
ΠΏΠΎ Π·Π°ΠΌΠ΅Π΄Π»Π΅Π½ΠΈΡ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² - 98,8 %. ΠΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΠ°Ρ Π°Π²ΡΠΎΡΡΠΊΠ°Ρ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠ° ΠΏΡΠ΅Π΄Π½Π°Π·Π½Π°ΡΠ΅Π½Π° Π΄Π»Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ Π² ΠΏΠΎΠ΄ΡΠΈΡΡΠ΅ΠΌΠ΅ ΠΠ’-Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ Π½Π΅Π²ΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ ΡΠΌΠ½ΠΎΠ³ΠΎ Π³ΠΎΡΠΎΠ΄Π°
Recognition of signs of Parkinson's disease based on the analysis of voice markers and motor activity
Π Π΅ΡΠ°Π΅ΡΡΡ Π·Π°Π΄Π°ΡΠ° ΠΠ’-Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π±ΠΎΠ»Π΅Π·Π½ΠΈ ΠΠ°ΡΠΊΠΈΠ½ΡΠΎΠ½Π° ΠΏΠΎ Π°Π½Π°Π»ΠΈΠ·Ρ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ Π³ΠΎΠ»ΠΎΡΠ°
ΠΈ Π·Π°ΠΌΠ΅Π΄Π»Π΅Π½ΠΈΡ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ². ΠΠΊΡΡΠ°Π»ΡΠ½ΠΎΡΡΡ Π·Π°Π΄Π°ΡΠΈ ΡΠ²ΡΠ·Π°Π½Π° Ρ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡΡ ΡΠ°Π½Π½Π΅ΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ
Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡ. ΠΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ ΠΌΠ΅ΡΠΎΠ΄ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ³ΠΎ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ Π±ΠΎΠ»Π΅Π·Π½ΠΈ ΠΠ°ΡΠΊΠΈΠ½ΡΠΎΠ½Π° Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΠΉ Π½Π° Π°Π½Π°Π»ΠΈΠ·Π΅ Π³ΠΎΠ»ΠΎΡΠΎΠ²ΡΡ
ΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ² ΠΈ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ Π² Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡΡ
ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Π½Π° ΠΈΠ·Π²Π΅ΡΡΠ½ΡΡ
Π½Π°Π±ΠΎΡΠ°Ρ
Π΄Π°Π½Π½ΡΡ
. ΠΠ΅ΡΠΎΠ΄Ρ. ΠΡΠΏΠΎΠ»ΡΠ·ΡΡΡΡΡ ΡΠ°ΡΡΠΎΡΠ½ΠΎ-Π²ΡΠ΅ΠΌΠ΅Π½Π½Π°Ρ ΡΡΠ½ΠΊΡΠΈΡ (ΡΡΠ½ΠΊΡΠΈΡ Π²Π΅ΠΉΠ²Π»Π΅ΡΠ°), ΡΡΠ½ΠΊΡΠΈΡ ΠΊΠ΅ΠΏΡΡΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΠ° ΠΠ΅ΠΉΠ΅ΡΠ°, Π°Π»Π³ΠΎΡΠΈΡΠΌ k-Π±Π»ΠΈΠΆΠ°ΠΉΡΠΈΡ
ΡΠΎΡΠ΅Π΄Π΅ΠΉ (k-Nearest Neighbors, KNN), Π°Π»Π³ΠΎΡΠΈΡΠΌ Π΄Π²ΡΡ
ΡΠ»ΠΎΠΉΠ½ΠΎΠΉ Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΠΉ ΡΠ΅ΡΠΈ Π΄Π»Ρ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ ΠΈ ΡΠ΅ΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π½Π° ΠΎΠ±ΡΠ΅Π΄ΠΎΡΡΡΠΏΠ½ΡΡ
Π½Π°Π±ΠΎΡΠ°Ρ
Π΄Π°Π½Π½ΡΡ
ΠΏΠΎ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΠ΅ΡΠΈ ΠΈ Π·Π°ΠΌΠ΅Π΄Π»Π΅Π½ΠΈΡ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ ΠΏΡΠΈ Π±ΠΎΠ»Π΅Π·Π½ΠΈ ΠΠ°ΡΠΊΠΈΠ½ΡΠΎΠ½Π°, Π° ΡΠ°ΠΊΠΆΠ΅ Π±Π°ΠΉΠ΅ΡΠΎΠ²ΡΠΊΠΈΠΉ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΎΡ Π΄Π»Ρ ΡΠ»ΡΡΡΠ΅Π½ΠΈΡ Π³ΠΈ-
ΠΏΠ΅ΡΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° KNN. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. ΠΠ»Π³ΠΎΡΠΈΡΠΌ KNN ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ Π΄Π»Ρ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ ΡΠ΅ΡΠΈ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ², ΡΠΎΡΠ½ΠΎΡΡΡ ΡΠ΅ΡΡΠ° 94,7 % Π΄ΠΎΡΡΠΈΠ³Π½ΡΡΠ° ΠΏΡΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ΅ Π±ΠΎΠ»Π΅Π·Π½ΠΈ ΠΠ°ΡΠΊΠΈΠ½ΡΠΎΠ½Π° ΠΏΠΎ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ Π³ΠΎΠ»ΠΎΡΠ°. ΠΠ»Π³ΠΎΡΠΈΡΠΌ Π±Π°ΠΉΠ΅ΡΠΎΠ²ΡΠΊΠΎΠΉ Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΠΉ ΡΠ΅ΡΠΈ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ Π΄Π»Ρ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ Π·Π°ΠΌΠ΅Π΄Π»Π΅Π½ΠΈΡ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ², ΠΎΠ½ Π΄Π°Π» ΡΠΎΡΠ½ΠΎΡΡΡ ΡΠ΅ΡΡΠ° 96,2 %. ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅. ΠΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π±ΠΎΠ»Π΅Π·Π½ΠΈ ΠΠ°ΡΠΊΠΈΠ½ΡΠΎΠ½Π° Π±Π»ΠΈΠ·ΠΊΠΈ ΠΊ ΠΌΠΈΡΠΎΠ²ΠΎΠΌΡ ΡΡΠΎΠ²Π½Ρ. ΠΠ° ΡΠΎΠΌ ΠΆΠ΅ Π½Π°Π±ΠΎΡΠ΅ Π΄Π°Π½Π½ΡΡ
ΠΏΠΎ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΠ΅ΡΠΈ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² ΠΎΠ΄ΠΈΠ½ ΠΈΠ· Π»ΡΡΡΠΈΡ
ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ Π·Π°ΡΡΠ±Π΅ΠΆΠ½ΡΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ ΡΠΎΡΡΠ°Π²Π»ΡΠ΅Ρ 95,8 %, Π° Π½Π° Π½Π°Π±ΠΎΡΠ΅ Π΄Π°Π½Π½ΡΡ
ΠΏΠΎ Π·Π°ΠΌΠ΅Π΄Π»Π΅Π½ΠΈΡ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² β 98,8 %. ΠΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΠ°Ρ Π°Π²ΡΠΎΡΡΠΊΠ°Ρ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠ° ΠΏΡΠ΅Π΄Π½Π°Π·Π½Π°ΡΠ΅Π½Π° Π΄Π»Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ Π² ΠΏΠΎΠ΄ΡΠΈΡΡΠ΅ΠΌΠ΅ ΠΠ’-Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ Π½Π΅Π²ΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ ΡΠΌΠ½ΠΎΠ³ΠΎ Π³ΠΎΡΠΎΠ΄Π°