Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30067
Data Mining in Oral Medicine Using Decision Trees

Authors: Fahad Shahbaz Khan, Rao Muhammad Anwer, Olof Torgersson, Göran Falkman

Abstract:

Data mining has been used very frequently to extract hidden information from large databases. This paper suggests the use of decision trees for continuously extracting the clinical reasoning in the form of medical expert-s actions that is inherent in large number of EMRs (Electronic Medical records). In this way the extracted data could be used to teach students of oral medicine a number of orderly processes for dealing with patients who represent with different problems within the practice context over time.

Keywords: Data mining, Oral Medicine, Decision Trees, WEKA.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1335258

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF

References:


[1] A Computerised Teaching Aid in Oral Medicine and Oral Pathology. Mats Jontell, Oral medicine, Sahlgrenska Academy, Göteborg University. Olof Torgersson, department of Computing Science, Chalmers University of Technology, Göteborg.
[2] T. Mitchell, "Decision Tree Learning", in T. Mitchell, Machine Learning, the McGraw-Hill Companies, Inc., 1997, pp. 52-78.
[3] P. Winston, "Learning by Building Identification Trees", in P. Winston, Artificial Intelligence, Addison-Wesley Publishing Company, 1992, pp. 423-442.
[4] Howard J. Hamilton-s CS Course: Knowledge Discovery in Databases. Accessed 06/06/12.
[5] http://www.cs.waikato.ac.nz/ml/weka/, accessed 06/05/21.
[6] http://grb.mnsu.edu/grbts/doc/manual/J48_Decision_T rees.html, accessed 06/06/12.
[7] Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.