Research on Carotid Ultrasound Feature Extraction Based on Machine Learning and its Role in Stroke Prediction

This paper studies the method of carotid ultrasound feature extraction based on machine learning and explores its application in stroke prediction. First, through high-quality carotid ultrasound image acquisition technology, a variety of carotid artery structure and blood flow characteristics are extracted, including important indicators such as carotid intima-media thickness (IMT), plaque morphology and blood flow velocity. To improve the accuracy of prediction, this study uses support vector machine algorithm to train and classify the extracted features and establish a stroke risk prediction model. Through the analysis of a large amount of clinical data, the model can effectively identify high-risk individuals based on carotid ultrasound images. Feature selection selects significant information through indicators such as information gain and mutual information to further improve the prediction accuracy of the model. The simulation results show that the accuracy of the proposed model in stroke prediction has reached more than 85%, which is significantly better than traditional methods and has strong generalization ability. This study verifies the important role of carotid ultrasound imaging in early warning of stroke.