A machine learning approach for carotid diseases using heart rate variability features

In the last few years the incidence of carotid diseases has been increasing rapidly. Atherosclerosis constitutes a major cause of morbidities and mortalities worldwide. The early detection of these diseases is considered necessary to avoid tragic consequences and automatic systems and algorithms can be a valid support for their diagnosis. The main objective of this study is to investigate and compare the performances of different machine learning techniques capable of detecting the presence of a carotid disease by analysing the Heart Rate Variability (HRV) parameters of opportune electrocardiographic signals selected from an appropriate database available online on the Physionet website. All the analyses are evaluated in terms of accuracy, precision, recall and F-measure.