CONCEPTUALİZİNG DATA ARCHİTECTURE FOR EXPERT SYSTEM IMPLEMENTATİON İN RESPİRATORY DİSEASE CLASSİFİCATİON

Abstract

Respiratory illnesses such as asthma, chronic obstructive pulmonary disease (COPD), pneumonia, and tuberculosis pose significant health risks and are leading causes of morbidity and mortality worldwide.Traditional methods of diagnosing respiratory illnesses rely heavily on subjective interpretation of symptoms and diagnostic tests, leading to variability and inconsistency in classification. In recent years, there has been growing interest in leveraging technology to enhance the diagnostic process and improve the accuracy of disease classification. Expert systems, computer-based systems that emulate human expertise and decision-making, offer promising avenues for achieving these goals. The successful implementation of expert systems for respiratory disease classification hinges upon the quality and structure of the underlying data architecture. Data architecture encompasses the organization, storage, and management of data within a system, providing the foundation for effective data processing and analysis. This paper aims to address this critical need by proposing a conceptual framework for the design and implementation of data architecture in expert systems for respiratory disease classification.