Machine Learning Models for Predicting Early Neurological Deterioration and Risk Classification of Acute Ischemic Stroke

Figures

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  Figure 1.. Flow chart for patients’ selection. IVT, intravenous thrombolysis; END, early neurological deterioration.

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  Figure 2.. Feature selection based on the LASSO regression. (A) LASSO coefficient profiles. Coefficient trends plot illustrating the changes in the regression coefficients of the selected features over different values of the regularization parameter λ in LASSO regression. (B) Error bar plot depicting the variation of cross-validated errors across different values of the regularization parameter λ in LASSO regression. The vertical dashed line represents the selected λ value of 0.03 based on the 1 standard error of the minimum criteria.

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  Figure 3.. Feature weight coefficient diagram.

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  Figure 4.. ROC curves for two predictive models: model 1 utilizing clinical indicators alone and model 2 combining clinical indicators with inflammatory cell ratios, in the prediction of post-thrombolysis END. Panels A and B display the ROC curves for the training and test sets.

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  Figure 5.. Nomogram for predicting risk classification of post-thrombolysis END. The nomogram was built with the NIHSS, systolic blood pressure (SBP), lymphocyte percentage (LYM%), NLR, PNR, and PLR. The probability associated with each predictor can be translated into scores based on the top scale points of the nomogram. By summing up the scores from all predictors, the corresponding prediction probability is obtained at the bottom of the nomogram.

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  Figure 6.. Calibration curve based on the entire dataset. The calibration curve visually evaluates the predictive model's alignment and accuracy by comparing the predicted probabilities with the actual outcomes. A well-calibrated curve with points close to the diagonal line indicates accurate predictions, while deviations suggest potential bias or miscalibration.