Development and validation of a random forest model to predict functional outcome in patients with intracerebral hemorrhage.

Abstract

To develop and validate a machine learning (ML)-based model to predict functional outcome in Chinese patients with intracerebral hemorrhage (ICH). This retrospective cohort study enrolled patients with ICH between November 2017 and November 2020. The follow-up period ended in February 2021. The study population was divided into training and testing sets with a ratio of 7:3. All variables were included in the least absolute shrinkage and selection operator (LASSO) regression for feature selection. The selected variables were incorporated into the random forest algorithm to construct the prediction model. The predictive performance of the model was evaluated via the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and calibration curve. A total of 412 ICH patients were included, with 288 in the training set, and 124 in the testing set. Twelve attributes were selected: neurological deterioration, Glasgow Coma Scale (GCS) score at 24 h, baseline GCS score, time from onset to the emergency room, blood glucose, diastolic blood pressure (DBP) change in 24 h, hematoma volume change in 24 h, systemic immune-inflammatory index (SII), systolic blood pressure (SBP) change in 24 h, serum creatinine, serum sodium, and age. In the testing set, the accuracy, AUC, sensitivity, specificity, PPV, and NPV of the model were 0.895, 0.964, 0.872, 0.906, 0.810, and 0.939, respectively. The calibration curves showed a good calibration capability of the model. This developed random forest model performed well in predicting 3-month poor functional outcome for Chinese ICH patients.