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...
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... The Turkish Journal of Electrical Engineering & Computer Sciences Volume 21, Issue 1 (2013).
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The concept of tumor heterogeneity being related to the course of the disease and clinical outcome in cancer patients draws additional attention in the era of personalized medicine (1). Current cancer treatment strategies are based on the site of origin of the primary tumor. However, it was shown that tumors developed from distinct cell types differ in their prognosis and response to cytotoxic therapies (2...
Data mining applications can be used in medical science and the Bioinformatics research field for diagnosis of critical diseases [1, 2]. Aside from other contracting diseases which end lives, breast cancer has probably become an intensely focused subject [3] for discovering cures aside from AIDS in the present decade. Breast cancer is a type of cancer disease arising from human breast tissue cells, usually from the lobules or the inner lining...
There are many risks that affect breast cancer. One of the reasons it is more common, is because we have better medical equipment that detects breast cancer at an earlier stage than before. An estimated 192,370 new cases of breast cancer will occu...
According to Breast Cancer Statistics, Breast cancer usually occurs in women between the ages of 35 and 65, even though fifty percent of all breast cancer is of women sixty-five and older (Breast Cancer Statistics, 2008). Although the cause of cancer is unknown, there are factors that increase the chances of getting it. These facto...
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Ford, D, et al. “Estimates of the Gene Frequency of BRCA1 and Its Contribution to Breast and
Abstract:- This paper presents a brief idea about data mining, data mining technology, and big data. The applications regarding data mining will also be discussed briefly. The main cause of data mining is to get different ideas, how to access big data by different tools.
H. Van Khuu, H.-KieLee, and J.-Liang Tsai. “Machine learning with neural networks and support vector machines”, 2005.
Power, Daniel J. "Understanding Data-Driven Decision Support Systems." Information Systems Management 25.2 (2008): 149-154. Business Source Complete. Web. 3 Apr. 2014.
Data mining consists of extracting interesting patterns representing knowledge from real-world databases. The software applications related with data mining includes various methodologies developed by both commercial and research organizations. Different data mining techniques used to...
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Description: Data Mining contains of several algorithms that fall into four different categories(Shobana et al. 2015)
HAND, D. J., MANNILA, H., & SMYTH, P. (2001).Principles of data mining. Cambridge, Mass, MIT Press.
Machine learning systems can be categorized according to many different criteria. We will discuss three criteria: Classification on the basis of the underlying learning strategies used, Classification on the basis of the representation of knowledge or skill acquired by the learner and Classification in terms of the application domain of the performance system for which knowledge is acquired.