Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:1041-1044. doi: 10.1109/EMBC48229.2022.9870927.
Carotid artery disease, the pathological condition of carotid arteries, is considered as the most significant cause of cerebral events and stroke. Carotid artery disease is considered as an inflammatory process, which involves the deposition and accumulation of atherosclerotic plaque inside the carotid intima, resulting in the narrowing of the arteries. Carotid artery stenosis (CAS) is either symptomatic or asymptomatic and its presence and location is determined by different imaging modalities, such as the carotid duplex ultrasound, the computed tomography angiography, the magnetic resonance angiography (MRA) and the cerebral angiography. The aim of this study is to present a machine learning model for the diagnosis and identification of individuals of asymptomatic carotid artery stenosis, using as input typical health data. More specifically, the overall model is trained with typical demographics, clinical data, risk factors and medical treatment data and is able to classify the individuals into high risk (Class 1-CAS group) and low risk (Class 0-non CAS group) individuals. In the presented study, we implemented a statistical analysis to check the data quality and the distribution into the two classes. Different feature selection techniques, in combination with classification schemes were applied for the development of our machine learning model. The overall methodology has been trained and tested using 881 cases (443 subjects in low risk class and 438 in high risk class). The highest accuracy 0.82 and an area under curve 0.9 were achieved using the relief feature selection technique and the random forest classification scheme.
颈动脉疾病，即颈动脉的病理状况，被认为是大脑事件和中风的最重要原因。颈动脉疾病被认为是一种炎症过程，涉及到动脉内膜中动脉粥样硬化斑块的沉积和积累，导致动脉狭窄。颈动脉狭窄（CAS）有症状或无症状，其存在和位置由不同的成像方式确定，如颈动脉双功超声、计算机断层血管造影、磁共振血管造影（MRA）和脑血管造影。本研究的目的是提出一种使用典型健康数据诊断和识别无症状颈动脉狭窄个体的机器学习模型。更具体地说，该整体模型使用典型的人口统计学、临床数据、风险因素和医疗数据进行训练，并能够将个体分为高风险（1 类-CAS 组）和低风险（0 类-非 CAS 组）个体。在本研究中，我们实施了统计分析来检查数据质量和分布到两个类别。我们应用了不同的特征选择技术，结合分类方案，开发了我们的机器学习模型。整体方法学使用 881 个案例（低风险类 443 个，高风险类 438 个）进行了训练和测试。使用 Relief 特征选择技术和随机森林分类方案，我们实现了最高的准确性 0.82 和曲线下面积 0.9。
