@article {Kadi201712,
	title = {Knowledge discovery in cardiology: A systematic literature review},
	journal = {International Journal of Medical Informatics},
	volume = {97},
	year = {2017},
	note = {cited By 6},
	pages = {12-32},
	abstract = {Context 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. Objective 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. Method 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. Results 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. {\textcopyright} 2016 Elsevier Ireland Ltd},
	doi = {10.1016/j.ijmedinf.2016.09.005},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988689033\&doi=10.1016\%2fj.ijmedinf.2016.09.005\&partnerID=40\&md5=9bd4f89bacb9b8dea94dd1f7eefef431},
	author = {Kadi, I. and Idri, A. and Fernandez-Aleman, J.L.}
}
@article { ISI:000390485500002,
	title = {Knowledge discovery in cardiology: A systematic literature review},
	journal = {INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS},
	volume = {97},
	year = {2017},
	month = {JAN},
	pages = {12-32},
	abstract = {Context: 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. Objective: 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. Method: 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. Results: 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. (C) 2016 Elsevier Ireland Ltd. All rights reserved.},
	issn = {1386-5056},
	doi = {10.1016/j.ijmedinf.2016.09.005},
	author = {Kadi, I. and Idri, A. and Fernandez-Aleman, J. L.}
}
@article {Anter2013158,
	title = {K-Schema: A new approach, based on the distribution of user queries, to create views to materialize in a hybrid integration system},
	journal = {Journal of Theoretical and Applied Information Technology},
	volume = {47},
	number = {1},
	year = {2013},
	note = {cited By 2},
	pages = {158-170},
	abstract = {The explosion of information technologies and telecommunications has made easy the access and production of information. That is how a very large mass of the latter has generated. This situation has made the integration systems a major need. Among these systems, there is the hybrid mediator. The latter interrogates one part of data on demand as in the virtual approach while charging, filtering and storing the second part, as views, in a local database. The choice of this second part is a critical task. This paper presents a selective approach, which based, essentially, to create these views, on the queries previously posed on the system. Based on the distribution of previous user queries, our approach extract all data most queried by users. The obtained data are classified as candidate views for materialization. Then selecting which one to materialize among all those created in the first step. {\textcopyright} 2005 - 2013 JATIT \& LLS. All rights reserved.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84872374571\&partnerID=40\&md5=c84d7e37d38a4f230cdf4c3a43c22954},
	author = {Anter, S. and Zellou, A. and Idri, A.}
}
