Diagnosis of Breast Cancer using Intelligent Techniques
H.S.Hota, Guru Ghasidas Central University ,Bilaspur (C.G) ,India
Manuscript received on January 12 2013. | Revised Manuscript Received on January 15 2013. | Manuscript published on January 25, 2013. | PP: 45-53 | Volume-1, Issue-3, January 2013. | Retrieval Number: C0134011313/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Breast cancer is a serious and life threatening disease due to its invasive and infiltrative character and is very commonly found in woman .An abnormal growth of cells in breast is the main cause of breast cancer actually this abnormal growth of cells can be of two types benign (Non-Cancerous) and malignant (Cancerous) ,these types must be diagnosed clearly for proper meditation and for proper treatment. A physician with full of experience and knowledge can deal complex problem in the breast cancer diagnosis process to identify disease but modern medical diagnosis system is totally based on data obtained through clinical and/or other test ,most of the decision related to a patient to find out disease is taken based on these data Better classification of a disease is a very crucial and challenging job , a small error can cause the problem because it is directly related to the life of a human being. In this research work ,various intelligent techniques including supervised Artificial Neural Network (ANN) ,unsupervised Artificial Neural Network ,Statistical and decision tree based have been applied to classify data related to breast cancer health care obtained from UCI repository site. The various individual models developed are tested and combined together to form ensemble model .Experimental works were done using MATLAB and SPSS Clementine software obtained results shows that ensemble model is better than individual models accuracy obtained in case of ensemble model is ,which is higher than all individual models ,however counter propogation network (CPN) is a competitive model among all other individual models and accuracy of this model is very near to that is obtained in case of I ensemble model. In order to reduce dimensionality of breast cancer data set a ranking based feature selection technique is applied with best ensemble model ,experimental result show that model has less accuracy with less number of features .Models are also analyzed in terms of other error measures like sensitivity and specificity.
Keywords: Decision Tree (DT),Supervised and Unsupervised Artificial Neural Network (ANN), Breast Cancer . Support Vector Machine (SVM).