Prediction of postoperative haemorrhage after cerebral tumour surgery using machine learning algorithms.

backgroundTraditional diagnostic methods used by neurosurgeons are limited in their ability to address complex interactions. These limitations have necessitated the use of advanced artificial intelligence approaches capable of analyzing multidimensional data with greater precision in neurosurgical clinics. Postoperative intracranial hemorrhage is a critical complication following cerebral tumor surgery, often associated with increased morbidity and mortality. This study aimed to predict the risk of postoperative intracerebral hemorrhage in patients undergoing intracranial tumor surgery by employing machine learning (ML) algorithms for risk stratification and identifying key contributing factors.

methodsThis retrospective study included 118 patients monitored in the neurosurgical intensive care unit between January 2024 and January 2025. The primary outcome was postoperative hemorrhage, defined as a radiologically confirmed hematoma ≥ 5 ml on brain CT within 24 h. Using a predefined set of clinical and biochemical parameters analyzed with SPSS and R, multiple ML algorithms were developed. To address class imbalance in the training data, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. Models were evaluated using metrics including Area Under the Curve (AUC), accuracy, and F1-score, with further assessment via calibration plots and Decision Curve Analysis (DCA).

resultsThe LightGBM model demonstrated a robust and balanced predictive performance, achieving a test AUC of 0.7451, an accuracy of 76.9%, a sensitivity of 77.8%, and an F1-score of 0.700. Platelet count (PLT), serum chloride (Cl), and the change in C-reactive protein from pre- to postoperative state (delta-CRP) emerged as the most influential predictors of hemorrhage. Model explainability was enhanced using SHAP and LIME analyses, and the model showed good calibration with potential clinical net benefit.

conclusionOur study suggests that ML algorithms, particularly LightGBM, show promise for predicting postoperative hemorrhage following brain tumor surgery. Biomarkers such as platelet count, chloride, and delta-CRP offer clinically meaningful insights for early risk detection. Once externally validated, the integration of such models into clinical decision support systems could potentially improve postoperative monitoring and patient outcomes.