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. 2022 Aug 31;10:944137. doi: 10.3389/fpubh.2022.944137

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Copyright © 2022 Chen, Zhang, Zhuang, Li, Zhang, Zhu, Yu and Yuan.

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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Selection of risk factors of infectious diseases using LASSO regression algorithm. A vertical line was plotted at the given lambda, selected by 10-fold cross-validation with minimum classification error and minimum classification error plus one standard error. For the optimal lambda that gives minimum classification error, 8 features with a non-0 coefficient were selected. (A) LASSO coefficient profiles of the candidate variables. (B) The binomial deviance with 95% CI (y-axis) was plotted against log (lambda) (bottom x-axis), when the number of included variables were changed (upper x-axis). (C) The AUCs with 95% CI (y-axis) were plotted against log (lambda) (bottom x-axis), when the number of included variables were changed (upper x-axis). LASSO, the Least Absolute Shrinkage and Selection Operator; CI, confidence interval; AUC, areas under the curve.