DIAGNOSIS AND PREDICTION OF CHOLECYSTITIS DEVELOPMENT ON THE BASIS OF NEURAL NETWORK ANALYSIS OF RISK FACTORS
Purpose. To develop an artificial neural network for diagnosing and predicting the development of cholecystitis based on an analysis of data on risk factors, and to explore the possibilities of its application in real clinical practice.Materials and methods. The collection of materials was held in a...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | Russian |
| Published: |
QUASAR, LLC
2017-12-01
|
| Series: | Исследования и практика в медицине |
| Subjects: | |
| Online Access: | https://www.rpmj.ru/rpmj/article/view/221 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850025055935791104 |
|---|---|
| author | V. A. Lazarenko A. E. Antonov |
| author_facet | V. A. Lazarenko A. E. Antonov |
| author_sort | V. A. Lazarenko |
| collection | DOAJ |
| description | Purpose. To develop an artificial neural network for diagnosing and predicting the development of cholecystitis based on an analysis of data on risk factors, and to explore the possibilities of its application in real clinical practice.Materials and methods. The collection of materials was held in at the hospitals of the city of Kursk and included a survey of 488 patients with hepatopancreatoduodenal diseases. 203 patients were suffering from cholecystitis, in 285 patients the diagnosis of cholecystitis was excluded. Analysis of risk factors’ data (such as sex, age, bad habits, profession, family relationships, etc.) was carried out using an internally developed artificial neural network (multilayer perceptron with hyperbolic tangent as the activation function). The computer program “System of Intellectual Analysis and Diagnosis of Diseases” was registered in accordance with established procedure (Certificate No. 2017613090).Results. The use of neural network analysis of data on risk factors in comparison with the processing of information that forms a clinical picture allows the diagnosis of a potential disease with cholecystitis before the onset of symptoms. The training of the artificial neural network with a quantitative output coding the age of probable hospitalization made it possible to generate an array of values, signifficantly (α ≤ 0.001) not differing from the empirical data. The difference between the mean calculated and mean empirical values was 0.45 for the training set and 1.75 for the clinical approbation group. The mean absolute error was within the range of 1.87–2.07 years.Conclusion. 1. The proposed new approach to the diagnosis and prognosis of cholecystitis has demonstrated its effectiveness, which is confirmed in clinical approbation by the levels of sensitivity (94.44%, m = 2.26) and specificity (80.6%, m = 3.9).2. The error in predicting the age of probable hospitalization of patients with cholecystitis did not exceed 2.29 and 2.38 years for p = 0.95 and p = 0.99, respectively. |
| format | Article |
| id | doaj-art-83fe68cd38c944af89961cbe1a5b2359 |
| institution | DOAJ |
| issn | 2410-1893 |
| language | Russian |
| publishDate | 2017-12-01 |
| publisher | QUASAR, LLC |
| record_format | Article |
| series | Исследования и практика в медицине |
| spelling | doaj-art-83fe68cd38c944af89961cbe1a5b23592025-08-20T03:00:57ZrusQUASAR, LLCИсследования и практика в медицине2410-18932017-12-0144677210.17709/2409-2231-2017-4-4-7171DIAGNOSIS AND PREDICTION OF CHOLECYSTITIS DEVELOPMENT ON THE BASIS OF NEURAL NETWORK ANALYSIS OF RISK FACTORSV. A. Lazarenko0A. E. Antonov1Kursk State Medical UniversityKursk State Medical UniversityPurpose. To develop an artificial neural network for diagnosing and predicting the development of cholecystitis based on an analysis of data on risk factors, and to explore the possibilities of its application in real clinical practice.Materials and methods. The collection of materials was held in at the hospitals of the city of Kursk and included a survey of 488 patients with hepatopancreatoduodenal diseases. 203 patients were suffering from cholecystitis, in 285 patients the diagnosis of cholecystitis was excluded. Analysis of risk factors’ data (such as sex, age, bad habits, profession, family relationships, etc.) was carried out using an internally developed artificial neural network (multilayer perceptron with hyperbolic tangent as the activation function). The computer program “System of Intellectual Analysis and Diagnosis of Diseases” was registered in accordance with established procedure (Certificate No. 2017613090).Results. The use of neural network analysis of data on risk factors in comparison with the processing of information that forms a clinical picture allows the diagnosis of a potential disease with cholecystitis before the onset of symptoms. The training of the artificial neural network with a quantitative output coding the age of probable hospitalization made it possible to generate an array of values, signifficantly (α ≤ 0.001) not differing from the empirical data. The difference between the mean calculated and mean empirical values was 0.45 for the training set and 1.75 for the clinical approbation group. The mean absolute error was within the range of 1.87–2.07 years.Conclusion. 1. The proposed new approach to the diagnosis and prognosis of cholecystitis has demonstrated its effectiveness, which is confirmed in clinical approbation by the levels of sensitivity (94.44%, m = 2.26) and specificity (80.6%, m = 3.9).2. The error in predicting the age of probable hospitalization of patients with cholecystitis did not exceed 2.29 and 2.38 years for p = 0.95 and p = 0.99, respectively.https://www.rpmj.ru/rpmj/article/view/221artificial neural networkneuronetmultilayer perceptrondiagnosisdiagnosticscholecystitisartificial intelligenceprognosis |
| spellingShingle | V. A. Lazarenko A. E. Antonov DIAGNOSIS AND PREDICTION OF CHOLECYSTITIS DEVELOPMENT ON THE BASIS OF NEURAL NETWORK ANALYSIS OF RISK FACTORS Исследования и практика в медицине artificial neural network neuronet multilayer perceptron diagnosis diagnostics cholecystitis artificial intelligence prognosis |
| title | DIAGNOSIS AND PREDICTION OF CHOLECYSTITIS DEVELOPMENT ON THE BASIS OF NEURAL NETWORK ANALYSIS OF RISK FACTORS |
| title_full | DIAGNOSIS AND PREDICTION OF CHOLECYSTITIS DEVELOPMENT ON THE BASIS OF NEURAL NETWORK ANALYSIS OF RISK FACTORS |
| title_fullStr | DIAGNOSIS AND PREDICTION OF CHOLECYSTITIS DEVELOPMENT ON THE BASIS OF NEURAL NETWORK ANALYSIS OF RISK FACTORS |
| title_full_unstemmed | DIAGNOSIS AND PREDICTION OF CHOLECYSTITIS DEVELOPMENT ON THE BASIS OF NEURAL NETWORK ANALYSIS OF RISK FACTORS |
| title_short | DIAGNOSIS AND PREDICTION OF CHOLECYSTITIS DEVELOPMENT ON THE BASIS OF NEURAL NETWORK ANALYSIS OF RISK FACTORS |
| title_sort | diagnosis and prediction of cholecystitis development on the basis of neural network analysis of risk factors |
| topic | artificial neural network neuronet multilayer perceptron diagnosis diagnostics cholecystitis artificial intelligence prognosis |
| url | https://www.rpmj.ru/rpmj/article/view/221 |
| work_keys_str_mv | AT valazarenko diagnosisandpredictionofcholecystitisdevelopmentonthebasisofneuralnetworkanalysisofriskfactors AT aeantonov diagnosisandpredictionofcholecystitisdevelopmentonthebasisofneuralnetworkanalysisofriskfactors |