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

Ovarian Cancer Detection based on Dimensionality Reduction Techniques and Genetic Algorithm