KFU. Card publication. Building a fuzzy expert system for assessing the severity of pneumonia

KAZAN
FEDERAL UNIVERSITY

- RUS
- 中文
- ES

BUILDING A FUZZY EXPERT SYSTEM FOR ASSESSING THE SEVERITY OF PNEUMONIA

Form of presentation

Articles in international journals and collections

Year of publication

2023

Язык

английский

Authors, employees of KFU

Akhmedova Alfira Mazitovna, author

Burnashev Rustam Arifovich, author

Enikeev Arslan Ilyasovich, author

Other authors

Bolsunovskaya Marina Vladimirovna, author

Authors, postgraduates KFU

Amer Ismail F O, postgraduate kfu

Authors, students KFU

Enikeeva Adelya Iskanderovna, author

Bibliographic description in the original language

Burnashev R.A., Enikeeva A., Amer I.F., Akhmedova A.M., Bolsunovskaya M.V., Enikeev A.I. Building a fuzzy expert system for assessing the severity of pneumonia // Lecture Notes in Networks and Systems, 2023, 544 LNNS, pp. 380–396

Annotation

The paper describes the process of building a fuzzy expert system for assessing the severity of pneumonia. The use of fuzzy logic is an urgent direction in cases of incomplete certainty when making a medical diagnosis. Human health depends on making the right decision, as it can be difficult for a doctor to choose the correct diagnosis and treatment of pneumonia. To develop a medical expert system, we considered scales and algorithms for assessing the prognosis of the severity of community-acquired pneumonia PORT (PSI), CURB/CRB-65 and SMART-COP/SMART-CO. We strive to contribute the results of our research to the development of medical software products, namely, to increase the efficiency of medical services using artificial intelligence. Python and Prolog programming languages were used to develop the client-server application. The Django framework was used to develop the client part, and the PySwip module was used to process knowledge bases. The knowledge base of the expert system was developed using the SWI-Prolog software environment, which supports the necessary software libraries that provide the construction of the graphical shell of the expert system, as well as dynamic processing of fuzzy rules. The paper outlines the main stages of the life cycle of creating a system using fuzzy logic, which include all the key stages of system design. To test the knowledge base, a graphical interface of the system was developed using the XPCE cross-platform library, which is included in the SWI-Prolog software environment. The purpose of the study is to develop and implement a software module using fuzzy logical inference in a medical information system.

Keywords

Expert systems; Fuzzy logic; Knowledge base; SWI-Prolog

The name of the journal

Lecture Notes in Computer Science

URL

https://www.scopus.com/record/display.uri?eid=2-s2.0-85138316696&doi=10.1007%2f978-3-031-16075-2_27&origin=inward&txGid=6a72d6a5baad287ca52b0ef7686c71b1

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=278375&p_lang=2

Full metadata record

Field DC	Value	Language
dc.contributor.author	Akhmedova Alfira Mazitovna	ru_RU
dc.contributor.author	Burnashev Rustam Arifovich	ru_RU
dc.contributor.author	Enikeev Arslan Ilyasovich	ru_RU
dc.contributor.author	Bolsunovskaya Marina Vladimirovna	ru_RU
dc.contributor.author	Enikeeva Adelya Iskanderovna	ru_RU
dc.contributor.author	Amer Ismail F O	ru_RU
dc.date.accessioned	2023-01-01T00:00:00Z	ru_RU
dc.date.available	2023-01-01T00:00:00Z	ru_RU
dc.date.issued	2023	ru_RU
dc.identifier.citation	Burnashev R.A., Enikeeva A., Amer I.F., Akhmedova A.M., Bolsunovskaya M.V., Enikeev A.I. Building a fuzzy expert system for assessing the severity of pneumonia // Lecture Notes in Networks and Systems, 2023, 544 LNNS, pp. 380–396	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=278375&p_lang=2	ru_RU
dc.description.abstract	Lecture Notes in Computer Science	ru_RU
dc.description.abstract	The paper describes the process of building a fuzzy expert system for assessing the severity of pneumonia. The use of fuzzy logic is an urgent direction in cases of incomplete certainty when making a medical diagnosis. Human health depends on making the right decision, as it can be difficult for a doctor to choose the correct diagnosis and treatment of pneumonia. To develop a medical expert system, we considered scales and algorithms for assessing the prognosis of the severity of community-acquired pneumonia PORT (PSI), CURB/CRB-65 and SMART-COP/SMART-CO. We strive to contribute the results of our research to the development of medical software products, namely, to increase the efficiency of medical services using artificial intelligence. Python and Prolog programming languages were used to develop the client-server application. The Django framework was used to develop the client part, and the PySwip module was used to process knowledge bases. The knowledge base of the expert system was developed using the SWI-Prolog software environment, which supports the necessary software libraries that provide the construction of the graphical shell of the expert system, as well as dynamic processing of fuzzy rules. The paper outlines the main stages of the life cycle of creating a system using fuzzy logic, which include all the key stages of system design. To test the knowledge base, a graphical interface of the system was developed using the XPCE cross-platform library, which is included in the SWI-Prolog software environment. The purpose of the study is to develop and implement a software module using fuzzy logical inference in a medical information system.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject		ru_RU
dc.title	Building a fuzzy expert system for assessing the severity of pneumonia	ru_RU
dc.type	Articles in international journals and collections	ru_RU