Use Of Machine Learning To Determine Predictors Of Intracerebral Hemorrhage Expansion

Background: Intracerebral hemorrhage (ICH) constitutes upto 40% mortality in first 30 days. Early identification of predictors of hematoma expansion (HE) may improve efforts to prevent its occurrence and improve clinical outcome. Methods: We identified patients with ICH and follow-up imaging. HE was defined as a combination of absolute volume increase of 6cc, new IVH, or proportional increase of 33% in our dataset on 72h follow up scan. Presence of IVH was also included in hematoma expansion. We evaluated the predictive ability of 3 machine learning classifiers, Random Forest, Support Vector Machine (with RBF kernel) and Logistic regression (with L1 regularization). The evaluation was done using a K-fold stratified cross validation to avoid overfitting. K was selected to be the number of subjects with HE. The features employed by classifiers were entirely based on the baseline imaging: Hematoma volume, Systolic BP, Diastolic BP, Black hole signs, Island signs, Blend signs, Fluid level, Swirl signs, Spot signs. Results: Our dataset comprised of 91 patients (n=21 HE, n=70 no HE). According to the area under the ROC (AUC), the two top performing classifiers were Support Vector Machine (AUC=0.66 CI 0.50-0.79) and Logistic Regression (AUC=0.64 CI 0.49-0.80). The statistical significance of the prediction is confirmed by the Mann-Whitney U test, p=0.01 and p=0.04 respectively. Random Forest did not reach statistical significance. Finally, we evaluated what were the highest and lowest weighted features across the cross-validation with Logistic Regression. The 3 top features were: presence of black hole and island signs and the systolic blood pressure. The 3 least useful features were: presence of spot and swirl signs and hematoma volume. Conclusion: Using our cohort, we developed a machine learning algorithm that predicts hematoma expansion using imaging features and blood pressure. MBL provided better sensitivity of these imaging markers compared with previous studies.