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 information from these large data sets. The exploitation and understanding of such tools are the core application areas of data mining. These tools are based on ideas from computer science, machine learning, mathematics and statistics. Discovering meaningful information and helpful knowledge from several vast datasets has thus developed a significant research domain [1, 2].
Data mining has attracted many researchers and analysts in the information industry and in modern society as a whole in the present decade, due to the availability of large amounts of data and the impending need for changing such data into meaningful information and knowledge. The useful information and knowledge gained can be used for applications ranging from medical science, bio-informatics, market analysis, and customer satisfaction, to production control and science exploration.
Clinical diagnosis of critical diseases is a domain of data mining research. Aside from other contracting diseases which end lives, breast cancer has probably become an intensely focused subject  for discovering cures aside from AIDS in the present decade. Breast cancer can be very harmful to women around the world because it can lead to the loss of a breast or can...