Cardiovascular Disease Preliminary Diagnosis Application Using SQL Queries: Filling Diagnostic Gaps in Resource-Constrained Environments
Cardiovascular diseases (CVDs) are chronic diseases associated with a high risk of mortality and morbidity. Early detection of CVD is crucial to initiating timely interventions, such as appropriate counseling and medication, which can effectively manage the condition and improve patient outcomes. Th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/3/1320 |
| _version_ | 1797319020888719360 |
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| author | Rafał Doniec Eva Odima Berepiki Natalia Piaseczna Szymon Sieciński Artur Piet Muhammad Tausif Irshad Ewaryst Tkacz Marcin Grzegorzek Wojciech Glinkowski |
| author_facet | Rafał Doniec Eva Odima Berepiki Natalia Piaseczna Szymon Sieciński Artur Piet Muhammad Tausif Irshad Ewaryst Tkacz Marcin Grzegorzek Wojciech Glinkowski |
| author_sort | Rafał Doniec |
| collection | DOAJ |
| description | Cardiovascular diseases (CVDs) are chronic diseases associated with a high risk of mortality and morbidity. Early detection of CVD is crucial to initiating timely interventions, such as appropriate counseling and medication, which can effectively manage the condition and improve patient outcomes. This study introduces an innovative ontology-based model for the diagnosis of CVD, aimed at improving decision support systems in healthcare. We developed a database model inspired by ontology principles, tailored for the efficient processing and analysis of CVD-related data. Our model’s effectiveness is demonstrated through its integration into a web application, showcasing significant improvements in diagnostic accuracy and utility in resource-limited settings. Our findings indicate a promising direction for the application of artificial intelligence (AI) in early CVD detection and management, offering a scalable solution to healthcare challenges in diverse environments. |
| first_indexed | 2024-03-08T04:00:47Z |
| format | Article |
| id | doaj.art-a9892b99e0f74437877fd898b50d366a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T04:00:47Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a9892b99e0f74437877fd898b50d366a2024-02-09T15:08:37ZengMDPI AGApplied Sciences2076-34172024-02-01143132010.3390/app14031320Cardiovascular Disease Preliminary Diagnosis Application Using SQL Queries: Filling Diagnostic Gaps in Resource-Constrained EnvironmentsRafał Doniec0Eva Odima Berepiki1Natalia Piaseczna2Szymon Sieciński3Artur Piet4Muhammad Tausif Irshad5Ewaryst Tkacz6Marcin Grzegorzek7Wojciech Glinkowski8Department of Medical Informatics and Artificial Intelligence, Faculty of Biomedical Engineering, Silesian University of Technology, 41-800 Zabrze, PolandDepartment of Medical Informatics and Artificial Intelligence, Faculty of Biomedical Engineering, Silesian University of Technology, 41-800 Zabrze, PolandDepartment of Medical Informatics and Artificial Intelligence, Faculty of Biomedical Engineering, Silesian University of Technology, 41-800 Zabrze, PolandDepartment of Medical Informatics and Artificial Intelligence, Faculty of Biomedical Engineering, Silesian University of Technology, 41-800 Zabrze, PolandInstitute for Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, GermanyInstitute for Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, GermanyDepartment of Medical Informatics and Artificial Intelligence, Faculty of Biomedical Engineering, Silesian University of Technology, 41-800 Zabrze, PolandInstitute for Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, GermanyThe Polish Telemedicine and eHealth Society, Targowa 39A/5, 03-728 Warsaw, PolandCardiovascular diseases (CVDs) are chronic diseases associated with a high risk of mortality and morbidity. Early detection of CVD is crucial to initiating timely interventions, such as appropriate counseling and medication, which can effectively manage the condition and improve patient outcomes. This study introduces an innovative ontology-based model for the diagnosis of CVD, aimed at improving decision support systems in healthcare. We developed a database model inspired by ontology principles, tailored for the efficient processing and analysis of CVD-related data. Our model’s effectiveness is demonstrated through its integration into a web application, showcasing significant improvements in diagnostic accuracy and utility in resource-limited settings. Our findings indicate a promising direction for the application of artificial intelligence (AI) in early CVD detection and management, offering a scalable solution to healthcare challenges in diverse environments.https://www.mdpi.com/2076-3417/14/3/1320ontologydatabasecardiovascular diseasesdiagnosisdecision support systems |
| spellingShingle | Rafał Doniec Eva Odima Berepiki Natalia Piaseczna Szymon Sieciński Artur Piet Muhammad Tausif Irshad Ewaryst Tkacz Marcin Grzegorzek Wojciech Glinkowski Cardiovascular Disease Preliminary Diagnosis Application Using SQL Queries: Filling Diagnostic Gaps in Resource-Constrained Environments Applied Sciences ontology database cardiovascular diseases diagnosis decision support systems |
| title | Cardiovascular Disease Preliminary Diagnosis Application Using SQL Queries: Filling Diagnostic Gaps in Resource-Constrained Environments |
| title_full | Cardiovascular Disease Preliminary Diagnosis Application Using SQL Queries: Filling Diagnostic Gaps in Resource-Constrained Environments |
| title_fullStr | Cardiovascular Disease Preliminary Diagnosis Application Using SQL Queries: Filling Diagnostic Gaps in Resource-Constrained Environments |
| title_full_unstemmed | Cardiovascular Disease Preliminary Diagnosis Application Using SQL Queries: Filling Diagnostic Gaps in Resource-Constrained Environments |
| title_short | Cardiovascular Disease Preliminary Diagnosis Application Using SQL Queries: Filling Diagnostic Gaps in Resource-Constrained Environments |
| title_sort | cardiovascular disease preliminary diagnosis application using sql queries filling diagnostic gaps in resource constrained environments |
| topic | ontology database cardiovascular diseases diagnosis decision support systems |
| url | https://www.mdpi.com/2076-3417/14/3/1320 |
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