Abstract
Cardiac autonomic neuropathy (CAN) is a disease that occurs as a result of nerve damage causing an abnormal control of heart rate. CAN is often associated with diabetes and is important, as it can lead to an increased morbidity and mortality of the patients. The detection and management of CAN is important since early intervention can prevent further complications that may lead to sudden death from myocardial infarction or rhythm disturbance. This paper is devoted to a review of work on developing data mining techniques for automated detection of CAN. A number of different categorizations of the CAN progression have been considered in the literature, which could make it more difficult to compare the results obtained in various papers. This is the first review proposing a comprehensive survey of all categorizations of the CAN progression considered in the literature, and grouping the results obtained according to the categorization being dealt with. This novel, thorough and systematic overview of all categorizations of CAN progression will facilitate comparison of previous results and will help to guide future work.
Highlights
Clinical applications of data mining techniques have been actively investigated
Cardiac autonomic neuropathy (CAN) is a disease that occurs as a result of nerve damage causing an abnormal control of heart rate
This is the first review proposing a comprehensive survey of all categorizations of the CAN progression considered in the literature, and grouping the results obtained according to the categorization being dealt with
Summary
Clinical applications of data mining techniques have been actively investigated. For preliminaries and background information on this broad area let us refer the readers to the monographs [1,2,3]. The present review article deals with recent contributions to this broad research area for the special case of cardiovascular autonomic neuropathy (CAN), which is a well-known complication associated with diabetes (cf [12,13,14]). The large impact of CVD associated with diabetes mellitus Type 1 and Type 2 has brought about the recommendation that people with diabetes should be regularly screened for the presence of comorbidities including autonomic nervous system dysfunction with the aim to decrease the incidence of cardiovascular related morbidity and mortality [16,17,18]. The increased risk of cardiac mortality due to arrhythmias makes screening of people with diabetes for autonomic neuropathy vital so that early detection, intervention and monitoring can occur [23]. Data mining methods are an important adjunct to medical research in identifying disease markers that allow early detection, prevention or treatment of disease. Data mining methods have been used extensively in health care research to build prediction models that provide additional information for improving health care outcomes [31,32,33]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
