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Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

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

R Alizadehsani¹, M Roshanzamir², M Abdar³

¹Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC 3216, Australia.

Scientific Data

|October 25, 2019

Summary

A new Coronary Artery Disease (CAD) database compiles 126 papers and 68 datasets from 1992-2018. This resource aims to advance machine learning for CAD diagnosis and treatment.

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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.