Beta
ManageSearchChatDiscover
146682

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

Article

Last updated: 22 Jan 2023

OverviewSimilar Items

Subjects

-

Tags

Electrical Engineering

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. 

DOI

10.21608/bfemu.2021.146682

Keywords

Machine Learning, knowledge base systems, Knowledge acquestion, Data mining

Authors

First Name

A.

Last Name

El-Alfy

MiddleName

E.

Affiliation

Department. of Educational Technology Specific Education Faculty., El-Mansoura University., Mansoura.,Egypt.

Email

-

City

Mansoura

Orcid

-

First Name

M.

Last Name

Bazeed

MiddleName

-

Affiliation

Department of Urology., Faculty of medicine., El-Mansoura University., Mansoura., Egypt.

Email

-

City

Mansoura

Orcid

-

Volume

25

Article Issue

3

Related Issue

21255

Issue Date

2000-09-01

Receive Date

2000-05-11

Publish Date

2021-02-05

Page Start

1

Page End

20

Print ISSN

1110-0923

Online ISSN

2735-4202

Article File

BFEMU_Volume 25_Issue 3_Pages 1-20.pdf

PDF . 240.7kB

Link

https://bfemu.journals.ekb.eg/article_146682.html

Detail API

https://bfemu.journals.ekb.eg/service?article_code=146682

Order

3

Type

Research Studies

Type Code

1,205

Publication Type

Journal

Publication Title

MEJ. Mansoura Engineering Journal

Publication Link

https://bfemu.journals.ekb.eg/

MainTitle

-

Details

Type

Article

Created At

22 Jan 2023