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

• Published in: Scientific data Vol. 6; no. 1; pp. 227 - 13
• Main Authors: Alizadehsani, R., Roshanzamir, M., Abdar, M., Beykikhoshk, A., Khosravi, A., Panahiazar, M., Koohestani, A., Khozeimeh, F., Nahavandi, S., Sarrafzadegan, N.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23.10.2019; Nature Publishing Group
• Subjects: 631/114/2164; 631/114/2401; 692/699/75; Artificial intelligence; Cardiovascular disease; Coronary artery; Coronary Artery Disease - diagnosis; Coronary vessels; Data Descriptor; Data Mining; Databases, Factual; Diagnosis; Heart diseases; Humanities and Social Sciences; Humans; Internet; Learning algorithms; Machine Learning; multidisciplinary; Science; Science (multidisciplinary); Software; User-Computer Interface
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-019-0206-3

We present the coronary artery disease (CAD) database, a comprehensive resource, comprising 126 papers and 68 datasets relevant to CAD diagnosis, extracted from the scientific literature from 1992 and 2018. These data were collected to help advance research on CAD-related machine learning and data mining algorithms, and hopefully to ultimately advance clinical diagnosis and early treatment. To aid users, we have also built a web application that presents the database through various reports. Measurement(s) coronary artery disease Technology Type(s) digital curation Factor Type(s) year • disease Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.9825680