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. 2021 Jun 28;8:663509. doi: 10.3389/fcvm.2021.663509

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Copyright © 2021 Yifan, Jianfeng and Jun.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Establishment of the random forest diagnostic model based on differential ferroptosis-related genes in acute myocardial infarction (MI). (A) Principal component analysis (PCA) of these differentially expressed ferroptosis-related genes as dimension reduction; (B) the comparison of three different supervised learning models (RF, random forest; LR, logistic regression; SVC, support vector classification); (C) the diagnostic performance of the predictive model in the test set; (D) the confusion matrix of the test set.