Breast Cancer Detection Using Supervised Machine Learning: A Comparative Analysis

Abstract

The rapidly changing lifestyle of the people in the modern era has led to a significant increase in their health issues. This busy lifestyle can delay the diagnosis of the disease which may at time be able to prove fatal. Breast cancer is one of the most prominent causes of deaths among women. However, the recent advancements in the technology have been proved to provide promising solutions to address this issue. The application of intelligent techniques can help in diagnosing breast cancer in early stages thereby reducing the chances of fatality. This has motivated several researchers to apply machine learning methods for the detection of breast cancer and as such it has become the locus of most of the ongoing research in recent times. The work presented in this paper aims to analyse the performance of multiple machine learning classifiers in detecting breast cancer. The obtained results clearly indicate the efficacy of linear support vector classifier and gradient boosting over other classifiers. The findings of this research can be further utilized for developing more efficient ensemble models as well as optimizing the performance of existing models thereby increasing their prediction accuracy.