Decision Tree Learning Approach for Knowledge Acquisition from a Medical Database.
El-Alfy, A., Bazeed, M. (2021). Decision Tree Learning Approach for Knowledge Acquisition from a Medical Database.. EKB Journal Management System, 25(3), 1-20. doi: 10.21608/bfemu.2021.146682
A. E. El-Alfy; M. Bazeed. "Decision Tree Learning Approach for Knowledge Acquisition from a Medical Database.". EKB Journal Management System, 25, 3, 2021, 1-20. doi: 10.21608/bfemu.2021.146682
El-Alfy, A., Bazeed, M. (2021). 'Decision Tree Learning Approach for Knowledge Acquisition from a Medical Database.', EKB Journal Management System, 25(3), pp. 1-20. doi: 10.21608/bfemu.2021.146682
El-Alfy, A., Bazeed, M. Decision Tree Learning Approach for Knowledge Acquisition from a Medical Database.. EKB Journal Management System, 2021; 25(3): 1-20. doi: 10.21608/bfemu.2021.146682

Decision Tree Learning Approach for Knowledge Acquisition from a Medical Database.

MEJ- Mansoura Engineering Journal
Article 3, Volume 25, Issue 3, September 2000, Page 1-20  XML PDF (235.09 K)
Document Type: Research Studies
DOI: 10.21608/bfemu.2021.146682
View on SCiNiTO View on SCiNiTO
Authors
A. E. El-Alfy* 1; M. Bazeed2
1Department. of Educational Technology Specific Education Faculty., El-Mansoura University., Mansoura.,Egypt.
2Department of Urology., Faculty of medicine., El-Mansoura University., Mansoura., Egypt.
Abstract
Automated knowledge acquisition (AKA) tools hold promise of preventing the process of acquiring knowledge from becoming the bottleneck in the development of knowledge base systems (KBSs). Most hospitals keep detailed records of patients containing descriptions of symptoms, diagnosis, prescribed medicine, and observed changes over time. Cumulatively, these records contain a wealth knowledge about medical diagnosis and treatment. The knowledge acquisition process would be significantly simplified if an automated system could look at these records, and extract valuable pieces of knowledge. Doctors would only need to verify the output of these knowledge acquisition tools rather than sit through hours of interview with knowledge engineers. This paper presents a new methodology for knowledge acquisition from databases. The proposed methodology coincides with RITIO algorithm 13) in the rule induction without drawing the decision tree and in the eliminating less effective attributes. It builds the decision tree when needed. The new methodology differs from the ID3' likes algorithms (15) and RITIO since it Gods the global optimal solutions via back tracking. It is tested on standard example and applied on a real world database. 
Keywords
Machine learning; knowledge base systems; Knowledge acquestion; Data mining
Statistics
Article View: 87
PDF Download: 165