Fig. 1 | Scientific Reports

Fig. 1

From: Development and validation of an interpretable machine learning model for early prediction in patients with diabetes and sepsis

Fig. 1The alternative text for this image may have been generated using AI.

Demographic and clinical feature selection using the LASSO binary logistic regression model. (A) This plot displays the LASSO coefficient profiles for 52 features across various log(lambda) values. The vertical line at lambda.min = 0.04151642, determined through five-fold cross-validation, indicates the optimal lambda. At this point, five features have non-zero coefficients, highlighting the most influential predictors in the model. (B) The binomial deviance curve is plotted against log(lambda) to select the optimal lambda for the LASSO model. The dotted lines indicate the lambda.min and its 1-SE range, guiding the choice of lambda that balances model performance and complexity. The lambda.min of 0.04151642 is central to this selection, ensuring effective regularization and feature selection in the model. LASSO, Least Absolute Shrinkage and Selection Operator; SE, standard error.

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