Assessment of Stroke Risk Based on Morphological Ultrasound Image Analysis with Conformal Prediction

Non-invasive ultrasound imaging of carotid plaques allows for the development of plaque image analysis in order to assess the risk of stroke. In our work, we provide reliable con dence measures for the assessment of stroke risk, using the Conformal Prediction framework. This framework provides a way for assigning valid con dence measures to predictions of classical machine learning algorithms. We conduct experiments on a dataset which contains morphological features derived from ultrasound images of atherosclerotic carotid plaques, and we evaluate the results of four di erent Conformal Predictors (CPs). The four CPs are based on Arti cial Neural Networks (ANNs), Support Vector Machines (SVMs), Naive Bayes classi cation (NBC), and k-Nearest Neighbours (k-NN). The results given by all CPs demonstrate the reliability and usefulness of the obtained con dence measures on the problem of stroke risk assessment.