Breast Cancer Detection Using Random Forest Supported by Feature Selection
Keywords:
Breast Cancer, Early Detection, Feature Selection, Mammography, Random ForestsAbstract
Breast cancer has been responsible for the loss of approximately 1.5 million lives over the past 35 years, despite considerable investments in mammography-based detection and treatment. This persistently high death rate underscores the urgency for improved strategies. Research consistently emphasizes the significance of detecting cancer at its early stages, ideally when the tumor size is confined to a modest 5-10 millimeters, thus minimizing the need for invasive procedures such as intensive chemotherapy or radiation. However, the current primary detection methods often fall short in identifying these small, elusive tumors, particularly when they are nestled within dense breast tissue. Consequently, there is a pressing need for more efficient screening techniques. In this study, we propose an innovative machine learning based methodology for Breast Cancer Detection that employs the Feature Selection-Aided Random Forest Algorithm. The research framework incorporates advanced feature selection techniques, such as Variance Inflation Factor (VIF), Model-based Feature Selection, Recursive Feature Elimination, and Univariate Feature Selection, to extract highly relevant features and uncover hidden patterns associated with tumors. Experimental results demonstrate the remarkable effectiveness of this approach, with feature selection facilitated by the Variance Inflation Factor (VIF) algorithm achieving 98.83% accuracy when evaluated on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. This approach effectively identifies the most appropriate features, significantly enhancing the breast cancer detection system's performance.
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