title: Remote health monitoring of heart failure with data mining via CART method on HRV features.
abstract: Disease management programs, which use no advanced information and computer technology, are as effective as telemedicine but more efficient because less costly. We proposed a platform to enhance effectiveness and efficiency of home monitoring using data mining for early detection of any worsening in patient's condition. These worsenings could require more complex and expensive care if not recognized. In this letter, we briefly describe the remote health monitoring platform we designed and realized, which supports heart failure (HF) severity assessment offering functions of data mining based on the classification and regression tree method. The system developed achieved accuracy and a precision of 96.39% and 100.00% in detecting HF and of 79.31% and 82.35% in distinguishing severe versus mild HF, respectively. These preliminary results were achieved on public databases of signals to improve their reproducibility. Clinical trials involving local patients are still running and will require longer experimentation.
authors.affiliation: Department of Biomedical, Electronic, and Telecommunication Engineering, University Federico II of Naples, Naples 80125, Italy. leandro.pecchia@unina.it
authors.forename: Leandro
authors.initials: L
authors.lastname: Pecchia
authors.forename: Paolo
authors.initials: P
authors.lastname: Melillo
authors.forename: Marcello
authors.initials: M
authors.lastname: Bracale
mesh_terms: D057225:Data Mining / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006333:Heart Failure / Q000503:physiopathology*; D006339:Heart Rate / Q000502:physiology*; D006699:Home Care Services; D006801:Humans; D008991:Monitoring, Physiologic / Q000379:methods*; D012372:ROC Curve; D012044:Regression Analysis; D015203:Reproducibility of Results