A remote health monitoring platform using data mining via the CART method achieved 96.39% accuracy and 100.00% precision in detecting heart failure on public databases of signals.
Does a remote health monitoring platform using data mining on HRV features accurately detect and assess heart failure severity?
A remote health monitoring platform using data mining on HRV features demonstrated high accuracy in detecting heart failure and assessing its severity using public databases.
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.
Pecchia et al. (Tue,) conducted a letter in Heart failure. Remote health monitoring platform with data mining via CART method was evaluated on Accuracy in detecting heart failure. A remote health monitoring platform using data mining via the CART method achieved 96.39% accuracy and 100.00% precision in detecting heart failure on public databases of signals.