CardioOWL: An ontology-driven expert system for diagnosing coronary artery diseases

Abstract

According to World Health Organization, coronary artery diseases are responsible for 17% of the death in the world. Diagnosing the disease in the right time could lower the danger that it may cause. This paper presents an expert system (CardioOWL) that has the ability to diagnose any kind of coronary artery diseases. CardioOWL supplies the expert with the diagnosing strategies that could be used and suggests the drugs and/or other required operations to be taken. CardioOWL depends on ontology knowledge about the patient's symptoms to build the knowledge base and then it utilizes Semantic Web Rule Language (SWRL) to deduce the suitable medicine and the required operation for the patient. The system was tested by some general practitioners using several test cases. The system proves to have very good precession and recall.

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

“…Other studies on the diagnosis of cardiovascular diseases based on ontologies reported an accuracy of 75.5% (10-fold cross-validation) in [8], sensitivity of 0.83 and precision of 0.87 in [7], and accuracy of more than 90% in [53].…”

“…A personalized early diagnosis system based on artificial intelligence (AI), ontology, and other medical information processing systems may be a great prevention measure. Ontologies and ontology database models have been applied to the diagnosis of CVD [7,8] and other diseases such as pneumonia [9]. Ontologies are formal representations of knowledge that can be used to define the structure and content of a database, allowing the representation of complex and hierarchical relationships between different concepts [10,11].…”

“…Ontologies allow for encapsulating domain-specific knowledge in human-interpretable and machine-interpretable formats. These formats could conform to the diagnostic guidelines established by healthcare professionals, using a natural language that describes the symptoms of diseases [7][8][9]15,18]. A recently popular alternative is large language models, which are trained on large sets of textual data and generate human-like texts, including diagnostic recommendations; however, they do not demonstrate accurate medical understanding and can introduce the risk of spreading misinformation [19,20].…”

Towards Cerebral Palsy Diagnosis : An-Ontology Based Approach

“…In the recent years, the basic technologies and techniques that have been used in disability and diseases diagnosis are Expert Systems (ES), Case-Based Reasoning (CBR), and Ontology. Expert Systems is used to support learning and decision support in diseases diagnoses [3] [4], which behave like expert human based on system knowledge and rules. Recently, different techniques and systems developed to diagnose diseases such CardioOWL Ontology-Driven ES [3], and OBESTDD Ontology-Based ES [4].…”

“…Expert Systems is used to support learning and decision support in diseases diagnoses [3] [4], which behave like expert human based on system knowledge and rules. Recently, different techniques and systems developed to diagnose diseases such CardioOWL Ontology-Driven ES [3], and OBESTDD Ontology-Based ES [4]. Case-Based Reasoning (CBR) is another effective technology in diseases diagnoses which benefits from previous problems and solutions to generate solution for the new cases.…”