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. 2022 Mar 16;12(3):722. doi: 10.3390/diagnostics12030722

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© 2022 by the authors.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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(Top) Types of performance evaluation metrics for ML-based CVD systems, (Bottom) Example of a ROC for multi-label-based CVD systems (Courtesy of AtheroPoint, Roseville, CA, USA) [84], PPV: positive predictive value; NPV: negative predictive value; FPR: false positive rate; FNR: false negative rate; BR: binary relevance; CC: classifier chain; LP: label powerset; MLARAM: multi-label adaptive resonance associative map; RakEL: random k-labelset; MLkNN: multi-label k-nearest neighbor; CVE: cardiovascular events; CAD: coronary artery disease; ACS: acute coronary syndrome; ROC: receiver operating characteristic; (a–f): different en-points used in the multi-label studies.

(Top) Types of performance evaluation metrics for ML-based CVD systems, (Bottom) Example of a ROC for multi-label-based CVD systems (Courtesy of AtheroPoint, Roseville, CA, USA) [84], PPV: positive predictive value; NPV: negative predictive value; FPR: false positive rate; FNR: false negative rate; BR: binary relevance; CC: classifier chain; LP: label powerset; MLARAM: multi-label adaptive resonance associative map; RakEL: random k-labelset; MLkNN: multi-label k-nearest neighbor; CVE: cardiovascular events; CAD: coronary artery disease; ACS: acute coronary syndrome; ROC: receiver operating characteristic; (a–f): different en-points used in the multi-label studies.