Breast Cancer Detection Using Machine Learning Algorithms

Abstract

Breast cancer (BC) is one among the disease occur in women through the globe. Early Diagnosis of the cancer, on the other hand, will save lives. Radiologists can tell whether the mammography scans show cancer or not, but they can fail 15% of the time. We suggest a new approach for detecting breast cancer with high precision in this article. Data mining techniques had a major role to play in the initialstage diagnosis of breast cancer. We suggest an approach in this paper for improving the accuracy and efficiency of the classifiers Decision Tree (J48), Naive Bayes (NB), and Sequential Minimal Optimization (SMO).The proposed approach uses two benchmark datasets to test and compare the classifiers: Wisconsin Breast Cancer (WBC) and Breast Cancer dataset. Considering that, the probability of instances belonging to the majority class is significantly high; algorithms are far more likely to assign unique findings to the majority class during the classification process. In this paper, we discuss such a dilemma. We use the data-level methodology that involves data resampling to minimize the impact of class imbalance. 10fold cross-validation is used to assess the results. The outcome of the models such as Precision, Recall, ROC curve, Standard Deviation (STD), and accuracy are used to evaluate the performance. Experimentations reveal that applying a resample filter improves the accuracy of the classifier, with SMO outperforms other classifiers in the WBC dataset whereas J48 outperforms the rest in the Breast Cancer dataset.