The ongoing severity of breast cancer and its death toll rate has continued to be a global concern. To this effect, the existing diagnosis and treatment are not efficient enough to successfully detect both the stage and type of breast cancer tumors at its early stage. Recent studies reported that 25.84% of all cancer-related death is from breast cancer. It also mentioned the number of new breast cancer cases amounted to 29.46% and 31.85% of the total new cancer case in Africa and Ethiopia respectively. Researchers in the area agreed that among the critical area to address any efficient early detection model of breast cancer is to improve values associated with the false positive (FP) and false negative (FN) with the confusion matrix. To address this problem, research efforts are continuing to present different solution approaches using differently advanced techniques using Artificial intelligence (AI), Machine learning (ML), Deep Learning (DL), and Computational Intelligence (CI). This research paper implements a comprehensive hybrid intelligent model using computational intelligence and machine learning for the early detection of breast cancer tumors. In effect, this research work address improving both the True Positive and True Negative Results.

Keywords: Breast tumor, Breast cancer, Hybrid, Computational intelligence, Genetic algorithms, Confusion metrix