GENETIC ALGORITHM AND RANDOM FOREST CLASSIFIER FUSION: A CUTTING-EDGE APPROACH FOR BREAST CANCER DIAGNOSIS

Abstract

Breast cancer was a significant cause of mortality in women worldwide, highlighting the importance of early detection in improving patient survival rates. Although machine learning algorithms had shown effectiveness in diagnosing breast cancer, there was still room for improvement. This paper introduced a ground-breaking method that combined Genetic Algorithms (GAs) with Random Forest Classifiers (RFCs) for breast cancer diagnosis. GA’s were used to select the most informative features from the breast cancer dataset, while RFCs were employed to classify the data into cancerous and non-cancerous cases. The proposed approach was evaluated on a publicly available breast cancer dataset, and the results demonstrated a remarkable accuracy of 79.31%, surpassing the accuracy of RFCs without GA-based feature selection (77.58%). This innovative approach held great promise in improving the accuracy of early diagnosis and potentially saving lives. By leveraging the strengths of GAs and RFCs, this novel approach offered an effective means of diagnosing breast cancer and had the potential to revolutionize early detection practices.