Expert systems in uroflowmetry data evaluation
Introduction. In the practice of an urologist, it is customary to assess the type of urination by two parameters: most often it is the effective volume of the bladder (V) and the maximum volume rate of urination (Qmax). Since the expert assessment of the digital characteristics of urine flow is ofte...
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| Format: | Article |
| Language: | Russian |
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State Budget Educational Institute of Higher Professional Education, Rostov State Medical University, Ministry Health of Russian Federation
2018-10-01
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| Series: | Vestnik Urologii |
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| Online Access: | https://www.urovest.ru/jour/article/view/214 |
| _version_ | 1797272131628695552 |
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| author | A. V. Ershov F. P. Kapsargin A. G. Berezhnoy M. P. Miltigashev |
| author_facet | A. V. Ershov F. P. Kapsargin A. G. Berezhnoy M. P. Miltigashev |
| author_sort | A. V. Ershov |
| collection | DOAJ |
| description | Introduction. In the practice of an urologist, it is customary to assess the type of urination by two parameters: most often it is the effective volume of the bladder (V) and the maximum volume rate of urination (Qmax). Since the expert assessment of the digital characteristics of urine flow is often ambiguous, they are not taken into consideration by some doctors and often remain without due attention. Today there is a tendency in medicine to objectify by quantification of clinical parameters. The main technology used to solve the tasks of data processing and analysis, as well as their classification and forecasting, are artificial neural networks. The aim of the work was to develop an expert system of urine flow rate data recognition based on neural network classifier.Materials and methods. The training of an artificial three-layer neural network of direct distribution occurred according 210 uroflowgrams and a multidimensional vector, characterized by 9 input parameters.Results. The system was tested on 40 examples ‒ uroflowgram data of patients who did not participate in neural network training. Despite this fact, the neural network has identified all the proposed examples correctly.Conclusions. A neural network method for recognition of uroflowmetry data of various diseases of the lower urinary tract is proposed. The space of informative features influencing the assessment of uroflowmetry data is formed. An expert system that classifies diseases (3 types of disorders) of the lower urinary tract with a 95% degree of confidence has been developed. |
| first_indexed | 2024-03-07T14:23:59Z |
| format | Article |
| id | doaj.art-2173a7b596c44a54a5a9336cffcb432c |
| institution | Directory Open Access Journal |
| issn | 2308-6424 |
| language | Russian |
| last_indexed | 2024-03-07T14:23:59Z |
| publishDate | 2018-10-01 |
| publisher | State Budget Educational Institute of Higher Professional Education, Rostov State Medical University, Ministry Health of Russian Federation |
| record_format | Article |
| series | Vestnik Urologii |
| spelling | doaj.art-2173a7b596c44a54a5a9336cffcb432c2024-03-06T08:56:03ZrusState Budget Educational Institute of Higher Professional Education, Rostov State Medical University, Ministry Health of Russian FederationVestnik Urologii2308-64242018-10-0163121610.21886/2308-6424-2018-6-3-12-16167Expert systems in uroflowmetry data evaluationA. V. Ershov0F. P. Kapsargin1A. G. Berezhnoy2M. P. Miltigashev3Krasnoyarsk State Medical University named after Prof. V.F. Voino-YasenetskiKrasnoyarsk State Medical University named after Prof. V.F. Voino-YasenetskiKrasnoyarsk State Medical University named after Prof. V.F. Voino-YasenetskiKrasnoyarsk State Medical University named after Prof. V.F. Voino-YasenetskiIntroduction. In the practice of an urologist, it is customary to assess the type of urination by two parameters: most often it is the effective volume of the bladder (V) and the maximum volume rate of urination (Qmax). Since the expert assessment of the digital characteristics of urine flow is often ambiguous, they are not taken into consideration by some doctors and often remain without due attention. Today there is a tendency in medicine to objectify by quantification of clinical parameters. The main technology used to solve the tasks of data processing and analysis, as well as their classification and forecasting, are artificial neural networks. The aim of the work was to develop an expert system of urine flow rate data recognition based on neural network classifier.Materials and methods. The training of an artificial three-layer neural network of direct distribution occurred according 210 uroflowgrams and a multidimensional vector, characterized by 9 input parameters.Results. The system was tested on 40 examples ‒ uroflowgram data of patients who did not participate in neural network training. Despite this fact, the neural network has identified all the proposed examples correctly.Conclusions. A neural network method for recognition of uroflowmetry data of various diseases of the lower urinary tract is proposed. The space of informative features influencing the assessment of uroflowmetry data is formed. An expert system that classifies diseases (3 types of disorders) of the lower urinary tract with a 95% degree of confidence has been developed.https://www.urovest.ru/jour/article/view/214urologyartificial neural networksuroflowmetrydisease recognition. |
| spellingShingle | A. V. Ershov F. P. Kapsargin A. G. Berezhnoy M. P. Miltigashev Expert systems in uroflowmetry data evaluation Vestnik Urologii urology artificial neural networks uroflowmetry disease recognition. |
| title | Expert systems in uroflowmetry data evaluation |
| title_full | Expert systems in uroflowmetry data evaluation |
| title_fullStr | Expert systems in uroflowmetry data evaluation |
| title_full_unstemmed | Expert systems in uroflowmetry data evaluation |
| title_short | Expert systems in uroflowmetry data evaluation |
| title_sort | expert systems in uroflowmetry data evaluation |
| topic | urology artificial neural networks uroflowmetry disease recognition. |
| url | https://www.urovest.ru/jour/article/view/214 |
| work_keys_str_mv | AT avershov expertsystemsinuroflowmetrydataevaluation AT fpkapsargin expertsystemsinuroflowmetrydataevaluation AT agberezhnoy expertsystemsinuroflowmetrydataevaluation AT mpmiltigashev expertsystemsinuroflowmetrydataevaluation |