Diagnosis of gastric carcinoma by classification on feature projections

Date
2004
Authors
Güvenir, H. A.
Emeksiz, N.
İkizler, N.
Örmeci, N.
Advisor
Instructor
Source Title
Artificial Intelligence in Medicine
Print ISSN
0933-3657
1873-2860
Electronic ISSN
Publisher
Elsevier
Volume
31
Issue
3
Pages
231 - 340
Language
English
Type
Article
Journal Title
Journal ISSN
Volume Title
Abstract

A new classification algorithm, called benefit maximizing classifier on feature projections (BCFP), is developed and applied to the problem of diagnosis of gastric carcinoma. The domain contains records of patients with known diagnosis through gastroscopy results. Given a training set of such records, the BCFP classifier learns how to differentiate a new case in the domain. BCFP represents a concept in the form of feature projections on each feature dimension separately. Classification in the BCFP algorithm is based on a voting among the individual predictions made on each feature. In the gastric carcinoma domain, a lesion can be an indicator of one of nine different levels of gastric carcinoma, from early to late stages. The benefit of correct classification of early levels is much more than that of late cases. Also, the costs of wrong classifications are not symmetric. In the training phase, the BCFP algorithm learns classification rules that maximize the benefit of classification. In the querying phase, using these rules, the BCFP algorithm tries to make a prediction maximizing the benefit. A genetic algorithm is applied to select the relevant features. The performance of the BCFP algorithm is evaluated in terms of accuracy and running time. The rules induced are verified by experts of the domain. © 2004 Elsevier B.V. All rights reserved.

Course
Other identifiers
Book Title
Keywords
Benefit maximization, Feature projection, Gastric carcinoma, Machine learning, Voting, Diagnosis, Digestive system, Genetic algorithms, Problem solving, Tumors, Carcinoma, Feature projection, Feature extraction, algorithm, article, cancer classification, cancer diagnosis, cost, gastroscopy, mathematical computing, medical record, prediction, priority journal, stomach carcinoma, Adolescent, Adult, Aged, Aged, 80 and over, Algorithms, Child, Child, Preschool, Diagnosis, Computer-Assisted, Female, Humans, Infant, Male, Middle Aged, Stomach Neoplasms
Citation
Published Version (Please cite this version)