Colon cancer classification using microarray data

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The British University in Dubai (BUiD)
A thesis presented on the classification of cancerous and normal tissue samples using microarray data. In treating cancer time is of the essence and early detection can dramatically increase the chances of survival. Imaging techniques, which are the prevalent method of detection and diagnosis, are only useful once the cancerous growth has become visible.However, if techniques that detect cancerous processes at a genetic level are utilized then the cancerous tissues could be identified, and the disease diagnosed much earlier, thus giving a far better prognosis.Therefore, the aim of this thesis is to evaluate the performance of a variety of different classification methods with a particular dataset containing genetic samples of both normal and cancerous biopsies of the colon tissue.A classifier will be recommended which is able to learn the patterns within the microarray data that best determines the classification of the samples.
cancerous, microarray data, colon tissue, machine learning, classification, feature reduction, Principal Component Analysis (PCA), neural networks, naive bayes