Cancer Classification Using Neural Network

Naturally, cells in human body grow and divide in a controlled way to produce more cells to maintain health. Cancer affects human body when abnormal cells divide without control and becomes able to invade other tissues. The genetic material (DNA) of these cells becomes damaged or changed that affects normal cell growth and division. Early diagnosis is of considerable significance of the physician's skills conducted based on their knowledge and experience yet an error might occur. A range of therapies have been provided by researchers already. Use of various Artificial Intelligence methods for medical diagnosis of diseases has recently become widespread. These intelligent systems help physicians as a diagnosis assistant. Now, various Artificial Neural Network, Rough Set, Decision Tree, Bayesian Network are very popular for this purpose. In this paper, Multi layer Feed Forward Neural Network was used to detect cancer from Microarray Data and UCI Machine Learning Data. Back Propagation Rule was used for training the model. Throughout this paper, two types of validations were performed: cross validation and new case testing for above two datasets with different combination of hidden layers and corresponding nodes. It was found that, NN model can classify the data with very good accuracy and this will lead to automated medical diagnosis system for the particular disease.