Decision Support Systems in Cardiology: A Systematic Review

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Cardiology

Background:

• Cardiovascular diseases (CVDs) pose a significant global health challenge.
• Accurate and timely diagnosis is crucial for effective CVD management.
• Intelligent decision support systems (IDSS) offer potential for improving diagnostic accuracy and efficiency.

Purpose of the Study:

• To review and identify prevalent approaches in IDSS for cardiovascular disease diagnosis.
• To assess the reported accuracy of these IDSS in the literature.

Main Methods:

• A systematic literature review was conducted.
• Databases searched included Scopus and Web of Science.
• Forty-one relevant publications were analyzed.

Main Results:

• Knowledge base, fuzzy logic, and artificial neural networks (ANNs) are the most frequently employed methodologies.
• These approaches are utilized for both diagnosis and prediction of cardiovascular diseases.
• The reviewed IDSS demonstrated high accuracy, reaching up to 98%.

Conclusions:

• Commonly used IDSS approaches in cardiology include knowledge bases, fuzzy logic, and ANNs.
• These systems show significant promise for accurate cardiovascular disease diagnosis and prediction.
• The high accuracy reported suggests the clinical utility of IDSS in managing cardiovascular conditions.