Congestive heart failure detection using random forest classifier. Issue 130 (July 2016) - Masetic, Zerina; Subasi, Abdulhamit
Highlights: Heartbeat classification is substantial for diagnosing heart failure. Machine learning methods classify normal and congestive heart failure (CHF). The random forest method gives 100% classification accuracy in detecting CHF. Abstract: Background and objectives: Automatic electrocardiogram (ECG) heartbeat classification is substantial for diagnosing heart failure. The aim of this paper is to evaluate the effect of machine learning methods in creating the model which classifies normal and congestive heart failure (CHF) on the long-term ECG time series. Methods: The study was performed in two phases: feature extraction and classification phase. In feature extraction phase, autoregressive (AR) Burg method is applied for extracting features. In classification phase, five different classifiers are examined namely, C4.5 decision tree, k -nearest neighbor, support vector machine, artificial neural networks and random forest classifier. The ECG signals were acquired from BIDMC Congestive Heart Failure and PTB Diagnostic ECG databases and classified by applying various experiments. Results: The experimental results are evaluated in several statistical measures (sensitivity, specificity, accuracy, F -measure and ROC curve) and showed that the random forest method gives 100% classification accuracy. Conclusions: Impressive performance of random forest method proves that it plays significant role in detecting congestive heart failure (CHF) and can be valuable in expressing knowledge useful in medicine.