A COMPARISON OF MACHINE LEARNING ALGORITHMS FOR MULTILABEL CLASSIFICATION OF CAN
This article is devoted to the investigation and comparison of several important machine learning algorithms in their ability to obtain multilabel classifications of the stages of cardiac autonomic neuropathy (CAN). Data was collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University. Our experiments have achieved better results than those published previously in the literature for similar CAN identification tasks.
machine learning, multilabel classification, cardiac autonomic neuropathy.