EVALUATION OF THE RISK OF STROKE WITH CONFIDENCE PREDICTIONS BASED ON ULTRASOUND CAROTID IMAGE ANALYSIS

Abstract

Conformal Predictors (CPs) are Machine Learning algorithms that can provide reliable confidence measures to their predictions. In this work, we make use of the Conformal Prediction framework for the assessment of stroke risk based on ultrasound images of atherosclerotic carotid plaques. For this application, images were recorded from 137 asymptomatic and 137 symptomatic plaques (symptoms are Stroke, Transient Ischaemic Attack (TIA), and Amaurosis Fugax (AF)). Two feature sets were extracted from the plaques; the first based on morphological image analysis and the second based on image texture analysis. Both sets were used in order to evaluate the performance of CPs on this problem. Four CPs were constructed using four popular classification methods, namely Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Naive Bayes Classification (NBC), and k -Nearest Neighbours. The results given by all CPs demonstrate the reliability and importance of the obtained confidence measures on the problem of stroke risk assessment.