Prediction of hematoma expansion using a random forest model with clinical data of patients with intracerebral hemorrhage

Purpose: Hematoma expansion (HE) for patients with intracerebral hemorrhage (ICH) has been shown to be a predictor of clinical neurological deterioration in ICH patients. As of now, there is no diagnosis which may indicate HE at the time of presentation. In this study, a Random Forest-based machine learning model with clinical data from ICH patients was developed and used as input to predict HE. Materials and Methods: 200 ICH patients with known hematoma evolution, were enrolled in this study. Data included brain volume, and hematoma volume based on non-contrast CT (NCCT) measurements; and the following patient specific clinical variables: age, sex, Glasgow Coma Scale score (GCS), ICH score, NIH Stroke Scale (NIHSS) and time from onset of ICH to initial NCCT. Random Forest machine learning model was developed to predict HE using 104/26 subjects training/testing split. Grid search strategy tuned the classifier parameters and a 5-fold cross-validation approach was used during training. The performance of model was evaluated by sensitivity, specificity, and Area Under the Curve (AUC). Results: The developed Random Forest model was able to predict HE with sensitivity of 0.846, specificity of 0.769, AUC of 0.807. Hematoma volume and time from onset of ICH to initial NCCT were the most important features, followed by NIHSS and brain volume. Conclusion: A Random Forest-based machine learning model with multiple clinical data from ICH patients as input performed well in predicting HE. Brain volume may be a new predictor of hematoma expansion.