XGBoost outperforms other machine learning models in diagnosing Sepsis-Associated Thrombocytopenia: a multicenter...

Sepsis-associated thrombocytopenia is a frequent complication of sepsis and is associated with poor clinical outcomes. Accurate diagnosis remains challenging, and machine learning approaches may offer improved diagnostic performance. The objective of this study was to conduct a comparative assessment of the performance of machine learning models (Random Forest, Artificial Neural Networks, Extreme Gradient Boosting, and Naive Bayes) in diagnosing sepsis-associated thrombocytopenia and to evaluate their diagnostic performance based on predefined performance criteria.

This retrospective cross-sectional study was conducted at two centers and utilized data from 1,447 sepsis patients extracted from electronic health records between January 2013 and December 2023. The dataset comprised demographic and clinical attributes together with laboratory test results. The data were partitioned into training and test sets in an 80:20 ratio. All models were trained on the training set, and 10-fold cross-validation was applied within the training set to assess internal performance consistency. Model performance was evaluated using accuracy, precision, F1 score, AUROC, and confusion matrix. The ten most significant variables were ranked using classifier-based feature importance and SHAP analysis.

Thrombocytopenia occurred in 772 (53.4%) of the 1,447 sepsis patients. Cross-validation results indicated that XGBoost exhibited superior performance, achieving an accuracy of 91.10% and an F1 score of 92.31%. ANN and RF achieved accuracies of 90.32% and 83.75%, with F1 scores of 91.51% and 85.32%, respectively. The AUROC values for cross-validation and test sets of RF, ANN, XGBoost, and Naive Bayes models exceeded 90%.

Among the evaluated algorithms, the XGBoost model demonstrated superior performance, achieving an AUROC of 98.60% in cross-validation and 97.50% in the test set, indicating the strongest performance metrics within internal validation.