Automatic Machine-Learning-Based Outcome Prediction in Patients With Primary Intracerebral Hemorrhage

Background: A predictive model can provide physicians, relatives, and patients the accurate information regarding the severity of disease and its predicted outcome. In this study, we used an automated machine-learning-based approach to construct a prognostic model to predict the functional outcome in patients with primary intracerebral hemorrhage (ICH). Methods: We retrospectively collected data on demographic characteristics, laboratory studies and imaging findings of 333 patients with primary ICH. The functional outcomes at the 1st and 6th months after ICH were defined by the modified Rankin scale. All of the attributes were used for preprocessing and for automatic model selection with Automatic Waikato Environment for Knowledge Analysis. Confusion matrix and areas under the receiver operating characteristic curves (AUC) were used to test the predictive performance. Results: Among the models tested, the random forest provided the best predictive performance for functional outcome. The overall accuracy for predicting the 1st month outcome was 83.1%, with 77.4% sensitivity and 86.9% specificity, and the AUC was 0.899. The overall accuracy for predicting the 6th month outcome was 83.9%, with 72.5% sensitivity and 90.6% specificity, and the AUC was 0.917. Conclusions: Using an automatic machine learning technique to predict functional outcome after ICH is feasible, and the random forest model provides the best predictive performance across all tested models. This prediction model may provide information regarding functional outcome for clinicians that will help provide appropriate medical care for patients and information for their caregivers.