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Comparing data mining with ensemble classification of breast cancer masses in digital mammograms

conference contribution
posted on 06.12.2017, 00:00 by S Ghassem Pour, NULL McLeodNULL McLeod, Brijesh VermaBrijesh Verma, A Maeder
Medical diagnosis sometimes involves detecting subtle indications of a disease or condition amongst a background of diverse healthy individuals. The amount of information that is available for discovering such indications for mammography is large and has been growing at an exponential rate, due to population wide screening programmes. In order to analyse this information data mining techniques have been utilised by various researchers. A question that arises is: do flexible data mining techniques have comparable accuracy to dedicated classification techniques for medical diagnostic processes? This research compares a model-based data mining technique with a neural network classification technique and the improvements possible using an ensemble approach. A publicly available breast cancer benchmark database is used to determine the utility of the techniques and compare the accuracies obtained.

History

Start Page

55

End Page

64

Number of Pages

10

Start Date

01/01/2012

Finish Date

01/01/2012

eISSN

1613-0073

Location

Sydney, Australia

Publisher

CEUR-WS, Sun SITE Central Europe operated under the umbrella of RWTH Aachen University

Place of Publication

Aachen, Germany

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Centre for Intelligent and Networked Systems (CINS); TBA Research Institute; University of Western Sydney;

Era Eligible

Yes

Name of Conference

Australasian Joint Conference on Artificial Intelligence;Australian Workshop on Artificial Intelligence in Health