title: Congestive heart failure detection using random forest classifier.
abstract: OBJECTIVE
  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.
authors.affiliation: International Burch University, Faculty of Engineering and Information Technologies, 71000 Sarajevo, Bosnia and Herzegovina. Electronic address: zmasetic@ibu.edu.ba.
authors.forename: Zerina
authors.initials: Z
authors.lastname: Masetic
authors.affiliation: Effat University, College of Engineering, Computer Science Department, Jeddah 21478, Saudi Arabia. Electronic address: absubasi@effatuniversity.edu.sa.
authors.forename: Abdulhamit
authors.initials: A
authors.lastname: Subasi
mesh_terms: D000465:Algorithms; D004562:Electrocardiography; D006333:Heart Failure / Q000175:diagnosis*; D006801:Humans; D060388:Support Vector Machine
keywords: Autoregressive (AR) modeling; Congestive heart failure (CHF); Electrocardiogram (ECG); Machine learning; Random forest