A C4.5 decision tree method using autoregressive feature extraction achieved 99.86% accuracy (AUC 0.998) in detecting congestive heart failure from long-term ECG signals.
Does a C4.5 decision tree method accurately detect congestive heart failure from long-term ECG time series?
A C4.5 decision tree machine learning model demonstrated high accuracy in detecting congestive heart failure from ECG signals.
Effect estimate: AUC 0.998
Automatic electrocardiogram (ECG) heart beat classification is significant for diagnosis of heart failures. The purpose of this study is to evaluate the effect of C4.5 decision tree method in creating the model that will detect and separate normal and congestive heart failures (CHF) on the long-term ECG time series. The research was conducted in two stages: feature extraction using autoregressive (AR) module and classification by applying C4.5 decision tree method. The ECG signals were obtained from BIDMC Congestive heart failure database and classified by applying different experiments. The experimental results showed that the proposed method reached 99.86% classification accuracy (sensitivity 99.77%, specificity 99.93%, area under the ROC curve 0.998) and has potential in detecting the congestive heart failures.
Mašetić et al. (Sun,) conducted a other in Congestive heart failure. C4.5 decision tree method with autoregressive feature extraction vs. Normal ECG signals was evaluated on Classification accuracy (AUC 0.998). A C4.5 decision tree method using autoregressive feature extraction achieved 99.86% accuracy (AUC 0.998) in detecting congestive heart failure from long-term ECG signals.