Ovarian Cancer Detection based on Dimensionality Reduction Techniques and Genetic Algorithm
This work addresses ovarian cancer diagnosis for medical applications, but it is incremental as it applies existing methods to a specific dataset.
The study tackled ovarian cancer detection by comparing feature selection methods (PCA and genetic algorithm) combined with classification techniques (LDA and neural networks) on serum SELDI mass spectra datasets, achieving up to 100% accuracy with genetic algorithm and neural networks.
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.