Objective:

To develop and validate an interpretable machine learning model for predicting stroke-associated pneumonia (SAP) risk in older patients with acute ischemic stroke (AIS), emphasizing the importance of early identification of high-risk patients.

Approach:

Key Findings:

• SVM achieved an accuracy of 0.773, sensitivity of 0.667, specificity of 0.798, F1 score of 0.524 (indicating moderate predictive performance), Brier score of 0.156, and AUC of 0.794 in the test set.

Interpretation:

An interpretable SVM-based machine learning model can effectively predict SAP risk in older AIS patients using routinely available clinical and laboratory data, which may enhance early intervention strategies.

Limitations:

• Single-center study may limit generalizability and diversity of the patient population.
• Retrospective design may introduce biases.

Conclusion:

The study presents a machine learning model that can assist in identifying high-risk older patients for early prophylactic management of SAP.

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