A Comparative Study of Breast Cancer Detection Based on SVM and MLP BPN Classifier

746 Words2 Pages

The breast cancer is a severe disease found among females all over the world. This is a type of cancer disease arising from human breast tissue cells, usually from the lobules or the inner lining of the milk ducts that provide the ducts with milk. A recent medical survey reveals that throughout the world breast cancer occurs in 22.9% of all cancers in women and it also causes 13.7% of cancer deaths in them. Breast cancer can be very harmful to all women around the world because it can lead to the loss of a breast or can even be fatal. Diagnosis of breast cancer disease is an important area of data mining research. Classification as an essential data mining process also helps in clinical diagnosis and analysis of this disease. In our work, different classification techniques are applied to the benchmark Breast Cancer Wisconsin dataset from the UCI machine language repository for detection of breast cancer. Principal component analysis (PCA) technique has been used to reduce the dimension of the dataset. Our objectives is to diagnose and analyze breast cancer disease with the help of two well-known classifiers, namely, MLP Backpropagation NN (MLP BPN) and Support Vector Machine (SVM) and, therefore assess their performance in terms of different performance measures like Precision, Recall, F-Measure, ROC Area etc.

Data is considered to be the core element in this era of technological advancement and information science. Vast amounts of data have been collected periodically for operational purposes in business, administration, banking, medical science, environmental protection, security and in politics. Such data sets are huge and complex as well. Basically we require robust, simple and computationally efficient tools to extract info...

... middle of paper ...

... The Turkish Journal of Electrical Engineering & Computer Sciences Volume 21, Issue 1 (2013).
[9] D. Nauck, F. Klawonn, and R. Kruse, Foundations of Neuro fuzzy Systems. Wiley, Chichester (1997).
[10] D. Venet, J. E. Dumont, V. Detours, Most random gene expression signatures are significantly associated with breast cancer outcome, PLoS Comput. Biol. 7, e1002240 (2011).
[11] D. Hanahan, R. A. Weinberg, Hallmarks of cancer: The next generation. Cell 144, 646–674 (2011).
[12] J. Han, M. Kamber, Data Mining: Concepts & Techniques, 2nd Ed., Morgan & Kaufmann (2005).
[13] R. Rojas: Neural Networks A Systematic Introduction, Springer-Verlag, Berlin (1996).
[14] Corinna C and Vapnik V. Support-Vector Networks. Machine Learning, Volume 20, Issue 3, pp. 273-297; 1995.
[15] Breast Cancer Wisconsin (Original) dataset, UCI machine language repository (1992).

Open Document