〔Abstract〕 Objective To study the best machine learning method for early prediction of hematoma expansion in hypertensive intracerebral hemorrhage based on head CT plain scan. Methods The CT images of 130 patients with cerebral hemorrhage were retrospectively analyzed, and the texture features of the head CT plain scan were extracted. The classifier was trained by selecting the features, and the six classic machine learning methods were cross-validated to evaluate the stability and performanceof predicting cerebral hemorrhage hematoma expansion. Results The prediction performance of support vector machine(SVM-Radial)(AUC 0.714±0.144, accuracy 0.723±0.109), generalized linear model(GLM) prediction performance(AUC 0.643±0.125, accuracy 0.587±0.136), random forest(RF) prediction performance(AUC 0.686±0.128, accuracy 0.680±0.130), k-nearest neighbor(kNN) prediction performance(AUC 0.657±7 C 15, accuracy 0.639±39 performance 19), gradient boosting tree algorithm(GBM) Prediction performance(AUC 0.718±0.141, accuracy 0.670±0.126), neural network(NNet) prediction performance(AUC 0.659±0.162, accuracy 0.680±0.130), in which support vector machines showed high prediction performance, generalized linear model showed low predictive performance. Conclusion Among the six machine learning methods based on cranial CT radiomics to predict early hematoma expansion in hypertensive intracerebral hemorrhage, support vector machine(SVM-Radial) has the best predictive performance and has potential clinical application value.