Lung cancer classification using genetic algorithm to optimize prediction models

Author

Diaz, Joey Mark ; Pinon, Raymond Christopher ; Solano, Geoffrey

Author_Institution

Univ. of the Philippines, Manila, Philippines

fYear

2014

fDate

7-9 July 2014

Firstpage

1

Lastpage

6

Abstract

Lung cancer is one of the most fatal types of cancer around the world. The World Cancer Research Fund International estimated that in 2012, 1.8 million new cases of this disease were diagnosed. Early diagnosis and classification of this condition prompts medical professionals on safer and more effective treatment of the patient. Availability of microarray technology has paved the way to exploring the genes and its association in various diseases like lung cancer. This study utilized genetic algorithm as a method of feature (genes) selection for the support vector machine and artificial neural network to classify lung cancer status of a patient. Genetic algorithm (GA) successfully identified genes that classify patient lung cancer status with notable predictive performance.

Keywords

cancer; genetic algorithms; medical diagnostic computing; neural nets; support vector machines; ANN; GA; SVM; World Cancer Research Fund International; artificial neural network; feature selection; genes selection; genetic algorithm; lung cancer status classification; microarray technology; prediction models; support vector machine; DNA; Robustness; Support vector machines; Surgery; GALGO; artificial neural networks; feature selection; genetic algorithm; microarray; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Information, Intelligence, Systems and Applications, IISA 2014, The 5th International Conference on

Conference_Location

Chania

Type

conf

DOI

10.1109/IISA.2014.6878770

Filename

6878770