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. 2020 Jan 9;9(2):e013696. doi: 10.1161/JAHA.119.013696

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© 2020 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

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Five steps for the development and internal validation of candidate modeling algorithm. ^†A modeling algorithm is the collection of steps that translate data into a predictive equation. This process only uses the derivation data set. The validation data set is not used until a final modeling algorithm is selected and applied to the full derivation data set. Candidate modeling algorithms for the current analysis were as follows: (1) logistic regression using forward variable selection, (2) logistic regression using backwards variable selection, (3) generalized additive logistic regression using forward variable selection, (4) penalized logistic regression with a lasso penalty, (5) penalized logistic regression with a ridge penalty, (6) random forests, and (7) gradient boosted decision trees.