Mining cancer data with discrete particle swarm optimization and rule pruning

Author

Liu, Yao ; Chung, Yuk Ying

Author_Institution

Sch. of Inf. Technol., Univ. of Sydney, Sydney, NSW, Australia

Volume

2

fYear

2011

fDate

9-11 Dec. 2011

Firstpage

31

Lastpage

34

Abstract

Cancer is one of the most the gravest problems facing mankind. In 2008, it is estimated that over 7.6 million lives have been claimed by cancer. Early and precise detection plays a key role in treating the disease and improve survivability of patient. Among data classification algorithms, discrete particle swarm optimization (DPSO), a technique based on standard PSO has proved to be competitive in predicting breast cancer, and in this paper, we implement a classifier using DPSO with new rule pruning procedure for detecting lung cancer and breast cancer, which are the most common cancer for men and women. Experiment shows the new pruning method further improves the classification accuracy, and the new approach is effective in making cancer prediction.

Keywords

cancer; data mining; lung; medical computing; particle swarm optimisation; pattern classification; DPSO; breast cancer prediction; cancer data mining; data classification algorithm; data classifier; discrete particle swarm optimization; disease; lung cancer detection; patient survivability; rule pruning; Accuracy; Breast cancer; Classification algorithms; Data mining; Lungs; Particle swarm optimization; Cancer; Classification; Data mining; Discrete particle swarm optomization; Rule pruning;

fLanguage

English

Publisher

ieee

Conference_Titel

IT in Medicine and Education (ITME), 2011 International Symposium on

Conference_Location

Cuangzhou

Print_ISBN

978-1-61284-701-6

Type

conf

DOI

10.1109/ITiME.2011.6132050

Filename

6132050