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Journal of Applied Sciences
  Year: 2012 | Volume: 12 | Issue: 17 | Page No.: 1792-1800
DOI: 10.3923/jas.2012.1792.1800
 
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Constrained Association Rules for Medical Data

Bakheet Aldosari, Ghada Almodaifer, Alaaeldin Hafez and Hassan Mathkour

Abstract:
The aim of the study is to develop a system for discovering interesting association rules from medical data sets for the purposes of prediction. The system addresses three problems: Medical data contain a combination of categorical and numerical attributes, processing bottlenecks are caused by large search spaces and the discovery of useful association rules that connect textual information with medical image features. The medical data set used comprised 80 patients’ records each containing 142 attributes (textual information, numeric data and mammogram image features). The system developed used constraint-based association rule mining with a frequent pattern growth algorithm, with rules being filtered using support, confidence and lift. Association rules were constrained to have a maximum number of attributes and covers were additionally used to produce a summarization by introducing a greedy algorithm. The number of rules prior to the rule cover was 4607; after applying the rule cover summarization, the number of rules was decreased to 4170. When a 90% minimum confidence value was specified, just five association rules resulted from the 4170 available. The constrained association rule mining technique was successfully applied to medical data that contain image features. Interesting and concise association rules were able to be discovered for prediction purposes, which should assist clinicians in their decision making.
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How to cite this article:

Bakheet Aldosari, Ghada Almodaifer, Alaaeldin Hafez and Hassan Mathkour, 2012. Constrained Association Rules for Medical Data. Journal of Applied Sciences, 12: 1792-1800.

DOI: 10.3923/jas.2012.1792.1800

URL: https://scialert.net/abstract/?doi=jas.2012.1792.1800

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