Cardiovascular Disease Preliminary Diagnosis Application Using SQL Queries: Filling Diagnostic Gaps in Resource-Constrained Environments

Rafał Doniec*, Eva Odima Berepiki, Natalia Piaseczna, Szymon Sieciński*, Artur Piet, Muhammad Tausif Irshad, Ewaryst J. Tkacz, Marcin Grzegorzek, Wojciech M. Glinkowski

*Corresponding author for this work


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.
Original languageEnglish
JournalApplied Sciences (Switzerland)
Issue number14
Pages (from-to)1320
Publication statusPublished - 05.02.2024

Research Areas and Centers

  • Centers: Center for Artificial Intelligence Luebeck (ZKIL)
  • Centers: Cardiological Center Luebeck (UHZL)

DFG Research Classification Scheme

  • 205-07 Medical Informatics and Medical Bioinformatics
  • 205-12 Cardiology, Angiology

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