Intelligent breast cancer recognition using particle swarm optimization and support vector machines

Breast cancer is the most common cancer among women, except for skin cancer, but early detection of breast cancer improves the chances of survivability. Data mining is widely used for this purpose. As technology develops, large number of breast tumour features are being collected. Using all these fe...

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Bibliographic Details
Published in:Journal of experimental & theoretical artificial intelligence 2016-11, Vol.28 (6), p.1021-1034
Main Authors: Ahmadi, Abbas, Afshar, Parnian
Format: Article
Language:English
Subjects:
Breast
Breast cancer
Cancer
Classification
Data mining
Feature extraction
Intelligent systems
Mammography
Optimization
particle swarm optimization
Sensitivity analysis
support vector machine
Support vector machines
Swarm intelligence
Tumors
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Summary:Breast cancer is the most common cancer among women, except for skin cancer, but early detection of breast cancer improves the chances of survivability. Data mining is widely used for this purpose. As technology develops, large number of breast tumour features are being collected. Using all these features for cancer recognition is expensive and time-consuming. Feature extraction is necessary for increasing the classification accuracy. The goal of this work is to recognise breast cancer using extracted features. To reach this goal, a combination of clustering and classification is used. Particle swarm optimization is used to recognise tumour patterns. The membership degree of each tumour to the patterns is calculated and considered as a new feature. Support vector machine is then employed to classify tumours. Finally this method is analysed in terms of its accuracy, specificity, sensitivity and CPU time consuming using Wisconsin Diagnostic Breast Cancer data set.
ISSN:0952-813X
1362-3079
DOI:10.1080/0952813X.2015.1055828