Early Diagnosis of Carotid Artery Disease based on non-imaging data

Carotid artery disease refers to the build up of atherosclerotic plaques into the carotid arteries. This pathological condition is responsible for cerebral events and stroke. The diagnosis of the disease is based on imaging techniques (carotid ultrasound, computed tomography angiography and magnetic resonance angiography) and the disease is either symptomatic or asymptomatic. The aim of the presented study is to early identify individuals of high risk for carotid artery disease through utilizing data driven techniques. More specifically, we utilize a dataset of 135 individuals with carotid artery disease and healthy ones, as it is defined with the use of carotid ultrasound. The dataset includes both typical medical records (demographics, risk factors) and biomarkers (biochemical, hematological) and is trained into a gradient boosting, aiming to detect the healthy participants and the individuals of high risk. The pipeline includes the pre-processing of the dataset, the handling of the class imbalance, the implementation of feature ranking techniques and finally the evaluation of the proposed pipeline. The model achieved an accuracy of 0.82 and a sensitivity and specificity of 0.68 and 0.92, respectively.