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DOI: 10.14569/IJACSA.2016.071034
PDF

Improved Association Rules Mining based on Analytic Network Process in Clinical Decision Making

Author 1: Shakiba Khademolqorani

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 10, 2016.

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Abstract: Association Rules Mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a problem of concern, as conventional mining algorithms often produce too many rules for decision makers to digest. In order to overcome this problem in clinical decision making, this paper concentrates on using Analytic Network Process method to improve the process of extracting rules. The rules provided by association rules, through group decision making of physicians and health experts, are used to organize and evaluate related features by analytic network process. The proposed method has been applied in the completed blood count based on real database. It generated interesting association rules useable and useful for medical diagnosis.

Keywords: Clinical Data Mining; Clinical Decision Making; Association Rules Mining; Analytic Network Process

Shakiba Khademolqorani, “Improved Association Rules Mining based on Analytic Network Process in Clinical Decision Making” International Journal of Advanced Computer Science and Applications(IJACSA), 7(10), 2016. http://dx.doi.org/10.14569/IJACSA.2016.071034

@article{Khademolqorani2016,
title = {Improved Association Rules Mining based on Analytic Network Process in Clinical Decision Making},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.071034},
url = {http://dx.doi.org/10.14569/IJACSA.2016.071034},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {10},
author = {Shakiba Khademolqorani}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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