  Background: Ventilator-associated pneumonia (VAP) in traumatic brain injury
(TBI) patients poses a significant mortality risk and imposes a considerable
financial burden on patients and healthcare systems. Timely detection and
prognostication of VAP in TBI patients are crucial to improve patient outcomes
and alleviate the strain on healthcare resources.
  Methods: We implemented six machine learning models using the MIMIC-III
database. Our methodology included preprocessing steps, such as feature
selection with CatBoost and expert opinion, addressing class imbalance with the
Synthetic Minority Oversampling Technique (SMOTE), and rigorous model tuning
through 5-fold cross-validation to optimize hyperparameters. Key models
evaluated included SVM, Logistic Regression, Random Forest, XGBoost, ANN, and
AdaBoost. Additionally, we conducted SHAP analysis to determine feature
importance and performed an ablation study to assess feature impacts on model
performance.
  Results: XGBoost outperformed the baseline models and the best existing
literature. We used metrics, including AUC, Accuracy, Specificity, Sensitivity,
F1 Score, PPV, and NPV. XGBoost demonstrated the highest performance with an
AUC of 0.940 and an Accuracy of 0.875, which are 23.4% and 23.5% higher than
the best results in the existing literature, with an AUC of 0.706 and an
Accuracy of 0.640, respectively. This enhanced performance underscores the
models' effectiveness in clinical settings.
  Conclusions: This study enhances the predictive modeling of VAP in TBI
patients, improving early detection and intervention potential. Refined feature
selection and advanced ensemble techniques significantly boosted model accuracy
and reliability, offering promising directions for future clinical applications
and medical diagnostics research.
