title: Automatic prediction of cardiovascular and cerebrovascular events using heart rate variability analysis.
abstract: BACKGROUND
  There is consensus that Heart Rate Variability is associated with the risk of vascular events. However, Heart Rate Variability predictive value for vascular events is not completely clear. The aim of this study is to develop novel predictive models based on data-mining algorithms to provide an automatic risk stratification tool for hypertensive patients.
  
  
  METHODS
  A database of 139 Holter recordings with clinical data of hypertensive patients followed up for at least 12 months were collected ad hoc. Subjects who experienced a vascular event (i.e., myocardial infarction, stroke, syncopal event) were considered as high-risk subjects. Several data-mining algorithms (such as support vector machine, tree-based classifier, artificial neural network) were used to develop automatic classifiers and their accuracy was tested by assessing the receiver-operator characteristics curve. Moreover, we tested the echographic parameters, which have been showed as powerful predictors of future vascular events.
  
  
  RESULTS
  The best predictive model was based on random forest and enabled to identify high-risk hypertensive patients with sensitivity and specificity rates of 71.4% and 87.8%, respectively. The Heart Rate Variability based classifier showed higher predictive values than the conventional echographic parameters, which are considered as significant cardiovascular risk factors.
  
  
  CONCLUSIONS
  Combination of Heart Rate Variability measures, analyzed with data-mining algorithm, could be a reliable tool for identifying hypertensive patients at high risk to develop future vascular events.
authors: Multidisciplinary Department of Medical, Surgical and Dental Sciences, Second University of Naples, Naples, Italy; SHARE Project, Italian Ministry of Education, Scientific Research and University, Rome, Italy.
authors: Paolo
authors: P
authors: Melillo
authors: Department of Translational Medical Sciences, University of Naples Federico II, Naples, Italy.
authors: Raffaele
authors: R
authors: Izzo
authors: Multidisciplinary Department of Medical, Surgical and Dental Sciences, Second University of Naples, Naples, Italy; SHARE Project, Italian Ministry of Education, Scientific Research and University, Rome, Italy.
authors: Ada
authors: A
authors: Orrico
authors: SHARE Project, Italian Ministry of Education, Scientific Research and University, Rome, Italy.
authors: Paolo
authors: P
authors: Scala
authors: Multidisciplinary Department of Medical, Surgical and Dental Sciences, Second University of Naples, Naples, Italy; SHARE Project, Italian Ministry of Education, Scientific Research and University, Rome, Italy.
authors: Marcella
authors: M
authors: Attanasio
authors: Department of Translational Medical Sciences, University of Naples Federico II, Naples, Italy.
authors: Marco
authors: M
authors: Mirra
authors: Department of Translational Medical Sciences, University of Naples Federico II, Naples, Italy.
authors: Nicola
authors: N
authors: De Luca
authors: School of Engineering, University of Warwick, Coventry, United Kingdom.
authors: Leandro
authors: L
authors: Pecchia
mesh_terms: D000368:Aged; D000465:Algorithms; D001331:Automation; D002318:Cardiovascular Diseases / Q000000981:diagnostic imaging / Q000503:physiopathology*; D002561:Cerebrovascular Disorders / Q000503:physiopathology*; D003663:Decision Trees; D005260:Female; D006339:Heart Rate; D006801:Humans; D008297:Male; D012372:ROC Curve; D014463:Ultrasonography