(untitled) (International Journal of Engineering and Computer Science, 2017)
Breast cancer is one of the second leading causes of cancer death in women. Despite the fact that cancer is preventable and curable in primary stages, the vast number of patients are diagnosed with cancer very late. Established methods of detecting and diagnosing cancer mainly depend on skilled physicians, with the help of medical imaging, to detect certain symptoms that usually appear in the later stages of cancer. The objective of this paper is to find the smallest subset of features that can guarantee highly accurate classification of breast cancer as either benign or malignant. Then a relative study on different cancer classification approaches viz. Naïve Bayes(NB), Logistic Regression(LR), Decision Tree(DT) classifiers are conducted where the time complexity of each of the classifier is also measured. Here, Logistic Regression classifier is concluded as the best classifier with the highest accuracy as compared to the other two classifiers