Breast Cancer Prediction System using Feature Selection and Data Mining Methods

Abstract

Cancer is the second most common cause of death worldwide with an estimated 7.9 million of deaths in 2007. This number is projected to rise further and reach 12 million deaths in 2030 which makes cancer a major public health issue. Early diagnosis and more recent tools of managing cancer have shown to significantly improve the chances of survival and have brought new hope to patients. The identity of cancer from various factors or symptoms is a multifaceted issue which is not free from false presumptions often accompanied by volatile effects. In this paper, we have proposed three different feature selection method rank search, genetic search and greedy step wise search methods to identify the potential attributes from the Breast Cancer dataset using the classification of heart attack using data mining techniques. This breast cancer databases was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg. Attributes 2 through 10 have been used to represent instances. Each instance has one of 2 possible classes: benign or malignant. Number of instances: 699 we have investigated six different classification data mining techniques such as BayesNet, AttributeSelectedClassifier, J48, ClassificationviaRegression, Logistic, and OneR. The result shows that the three different set of potential attributes are obtained through rank search, genetic search and greedy step. It is observed that the performance and the time taken by each classification algorithms are significantly improved after feature selection and the bayesnet classifier outperforms the remaining algorithms used in this paper. Keywords: Breast Cancer, Data Mining, Classification, BayesNet, ClassificationviaRegression, Logistic, Rank Search, Genetic Search , Greedy Stepwise Search