Feature Selection Using Correlation Analysis for Accurate Breast Cancer Diagnosis

Abstract

Breast cancer is a sickness that can affect women when some cells in their breasts grow abnormally. It's a serious problem and is one of the main reasons why women pass away. It's hard to accurately diagnose because it's a complex disease, and treatments and patients vary. Thankfully, there have been important discoveries in how to find and treat breast cancer. Now, about 89 out of every 100 women with breast cancer can survive if they find it early. So, we need a special kind of technology that can find breast cancer when it's just starting and reduce the chance of cancer coming back. Scientists use computer programs called “machine learning” to help find breast cancer in lots of data. The fundamental purpose of this research initiative was to prudently apply feature selection methodologies, while integrating the examination of correlations among input features. These selected significant features were subsequently employed in conjunction with a classification method.