Risk factors for relapse and nomogram for relapse probability prediction in patients with minor ischemic stroke

Figure 2 Least absolute shrinkage and selection operator regression analysis. Demographic and clinical feature selection using the Least absolute shrinkage and selection operator binary logistic regression model is shown. A: Optimal parameter (lambda) selection in the least absolute shrinkage and selection operator (LASSO) model used fivefold cross-validation via minimum criteria. The partial likelihood deviance (binomial deviance) curve was plotted vs log (lambda). Dotted vertical lines are drawn at the optimal values by using the minimum criteria and the 1 standard error (SE) of the minimum criteria (the 1-SE criteria); B: LASSO coefficient profiles of the 27 features. A coefficient profile plot is produced against the log (lambda) sequence. Then a vertical line is drawn at the value selected using fivefold cross-validation, where optimal lambda results in seven features with nonzero coefficients.