A database for using machine learning and data mining techniques for coronary artery disease diagnosis

Area of Science:

• Medical Informatics
• Cardiovascular Research
• Data Science

Background:

• Coronary Artery Disease (CAD) diagnosis relies on complex data analysis.
• Advancements in machine learning and data mining offer potential for improved CAD detection.
• A centralized, curated resource is needed to facilitate research in this area.

Purpose of the Study:

• To create a comprehensive database of scientific literature and datasets relevant to Coronary Artery Disease (CAD).
• To support the development and validation of machine learning and data mining algorithms for CAD diagnosis.
• To facilitate early clinical diagnosis and treatment of CAD through data-driven insights.

Main Methods:

• Systematic extraction of 126 relevant papers from scientific literature published between 1992 and 2018.
• Compilation of 68 distinct datasets pertaining to CAD diagnosis.
• Development of a web application for user-friendly access and reporting of the database contents.

Main Results:

• A curated database comprising 126 papers and 68 datasets focused on Coronary Artery Disease (CAD).
• The database covers research spanning from 1992 to 2018, providing historical context and recent findings.
• An accompanying web application is available for data exploration and analysis.

Conclusions:

• The Coronary Artery Disease (CAD) database serves as a valuable resource for researchers in machine learning and data mining.
• This initiative is expected to accelerate progress in CAD diagnosis and treatment strategies.
• The integrated web application enhances accessibility and utility for the scientific community.