Title :
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
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;
Conference_Titel :
Information, Intelligence, Systems and Applications, IISA 2014, The 5th International Conference on
Conference_Location :
Chania
DOI :
10.1109/IISA.2014.6878770
