  In this research, we have two serum SELDI (surface-enhanced laser desorption
and ionization) mass spectra (MS) datasets to be used to select features
amongst them to identify proteomic cancerous serums from normal serums.
Features selection techniques have been applied and classification techniques
have been applied as well. Amongst the features selection techniques we have
chosen to evaluate the performance of PCA (Principal Component Analysis ) and
GA (Genetic algorithm), and amongst the classification techniques we have
chosen the LDA (Linear Discriminant Analysis) and Neural networks so as to
evaluate the ability of the selected features in identifying the cancerous
patterns. Results were obtained for two combinations of features selection
techniques and classification techniques, the first one was PCA+(t-test)
technique for features selection and LDA for accuracy tracking yielded an
accuracy of 93.0233 % , the other one was genetic algorithm and neural network
yielded an accuracy of 100%. So, we conclude that GA is more efficient for
features selection and hence for cancerous patterns detection than PCA
technique.
