Extracting temporal rules from medical data

Meamarzadeh, H; Khayyambashi, M; Saraee, MH

Abstract

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.

Citation

Meamarzadeh, H., Khayyambashi, M., & Saraee, M. (2009, November). Extracting temporal rules from medical data. Presented at The 2009 International Conference on Computer Technology and Development, Kota, Kinabalu, Malaysia

• Presentation Conference Type: Other
• Conference Name: The 2009 International Conference on Computer Technology and Development
• Conference Location: Kota, Kinabalu, Malaysia
• Start Date: Nov 13, 2009
• End Date: Nov 15, 2009
• Publication Date: Jan 1, 2009
• Deposit Date: Oct 27, 2011
• Book Title: 2009 International Conference on Computer Technology and Development
• DOI: https://doi.org/10.1109/ICCTD.2009.72
• Publisher URL: http://www.computer.org/portal/web/csdl/doi/10.1109/ICCTD.2009.72
• Related Public URLs: http://www.computer.org/portal/web/csdl/doi/10.1109/ICCTD.2009.72
• Additional Information: Event Type : Conference