Breast Cancer Prediction System using Feature Selection and Data Mining Methods
Main Article Content
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.
Â
Keywords: Breast Cancer, Data Mining, Classification, BayesNet, ClassificationviaRegression, Logistic, Rank Search, Genetic Search , Greedy
Stepwise Search
Downloads
Article Details
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.