title: Knowledge discovery in cardiology: A systematic literature review.
abstract: Data mining (DM) provides the methodology and technology needed to transform huge amounts of data into useful information for decision making. It is a powerful process employed to extract knowledge and discover new patterns embedded in large data sets. Data mining has been increasingly used in medicine, particularly in cardiology. In fact, DM applications can greatly benefit all those involved in cardiology, such as patients, cardiologists and nurses.
  
  
  
  The purpose of this paper is to review papers concerning the application of DM techniques in cardiology so as to summarize and analyze evidence regarding: (1) the DM techniques most frequently used in cardiology; (2) the performance of DM models in cardiology; (3) comparisons of the performance of different DM models in cardiology.
  
  
  
  We performed a systematic literature review of empirical studies on the application of DM techniques in cardiology published in the period between 1 January 2000 and 31 December 2015.
  
  
  
  A total of 149 articles published between 2000 and 2015 were selected, studied and analyzed according to the following criteria: DM techniques and performance of the approaches developed. The results obtained showed that a significant number of the studies selected used classification and prediction techniques when developing DM models. Neural networks, decision trees and support vector machines were identified as being the techniques most frequently employed when developing DM models in cardiology. Moreover, neural networks and support vector machines achieved the highest accuracy rates and were proved to be more efficient than other techniques.
authors.affiliation: Software Project Management Research Team, ENSIAS, Mohammed V University in Rabat, Morocco. Electronic address: ilham.kadi@um5s.net.ma.
authors.forename: I
authors.initials: I
authors.lastname: Kadi
authors.affiliation: Department of Informatics and Systems, Faculty of Computer Science, University of Murcia, Spain. Electronic address: ali.idri@um5.ac.com.
authors.forename: A
authors.initials: A
authors.lastname: Idri
authors.affiliation: Department of Informatics and Systems, Faculty of Computer Science, University of Murcia, Spain. Electronic address: aleman@um.es.
authors.forename: J L
authors.initials: JL
authors.lastname: Fernandez-Aleman
mesh_terms: D002309:Cardiology / Q000193:education*; D057225:Data Mining / Q000379:methods*; D003663:Decision Trees; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D060388:Support Vector Machine
keywords: Cardiology; Data mining; Knowledge extraction; Medical tasks