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Research Article | Open Access |

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) · Vol. 7, No. 10 · Published 2016

DOI: https://doi.org/10.14569/IJACSA.2016.071034

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

How to Cite this Article

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

Khademolqorani, Shakiba. "Improved Association Rules Mining based on Analytic Network Process in Clinical Decision Making." International Journal of Advanced Computer Science and Applications, vol. 7, no. 10, 2016, https://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},
  volume    = {7},
  number    = {10},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Shakiba Khademolqorani},
  doi       = {10.14569/IJACSA.2016.071034},
  url       = {https://doi.org/10.14569/IJACSA.2016.071034}
}

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