Extracting temporal rules from medical data

Meamarzadeh, H, Khayyambashi, M and Saraee, MH ORCID: https://orcid.org/0000-0002-3283-1912 2009, Extracting temporal rules from medical data , in: The 2009 International Conference on Computer Technology and Development, 13 - 15 November 2009, Kota, Kinabalu, Malaysia.

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Trauma is the main leading cause of death in children; we need a tool to prevent and predict the outcome in these patients. Data mining is the science of extracting the useful information from a large amount of data sets or databases that leads to statistical and logical analysis and looking for patterns that could help the decision makers. In This paper we offer an approach for using data mining in classifying mortality rate related to accidents in children under 15. These data were gathered from the patient files which were recorded in the medical record section of the Alzahra Hospital in Isfahan. The data mining methods in use are decision tree and Bayes' theorem. Applying DM techniques to the data brings about very interesting and valuable results. It is concluded that in this case, comparing the result of evaluating the models on test set, decision tree works better than Bayes' theorem. In this paper, we have used Clementine12.0 for creating the models.

Item Type: Conference or Workshop Item (Paper)
Themes: Health and Wellbeing
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: Proceedings of The 2009 International Conference on Computer Technology and Development
Publisher: IEEE Computer Society
Refereed: Yes
Related URLs:
Depositing User: Prof. Mo Saraee
Date Deposited: 27 Oct 2011 08:43
Last Modified: 16 Feb 2022 13:19
URI: https://usir.salford.ac.uk/id/eprint/18699

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