Breast cancer is type of chronic disease due to which a huge number of female patients are losing their life’s every year and this is increasing with an rapid rate, so to control this type of disease at an early stage and appropriate diagnosis is required in order to reduce the number of deaths and provide a new life to the patients. Here machine learning plays a vital role where various methods like classification, clustering and ensemble methods are quite useful for the detection of such type of chronic disease. This paper is written with an aim to enhance the prediction accuracy where a novel approach is proposed in which the classifiers like Naïve Bayes, J48 and Logistic Regression are combined with K-Means Clustering algorithm and Bagging and the experimental results shows a significant increase in the prediction accuracy and reduced error rate. This study is conducted on the Wisconsin Breast Cancer Diagnostic Dataset (WDBC).
Volume 11 | 06-Special Issue
Pages: 544-554