Evolutionary Neural Networks versus Adaptive Resonance Theory Net for Breast Cancer Diagnosis

Breast cancer is the most common cancer among human females worldwide. Early detection of breast cancer is the only solution to reduce the breast cancer mortality. Machine learning models such as neural networks are one of the well-studied tools for the early detection of breast cancer. In this work, we implemented a supervised model called MLP and an unsupervised model called ART-1 net. We trained the MLP with two different evolutionary optimization techniques such as Biogeography-based Optimization (BBO) and Particle Swarm Optimization (PSO). The resulting models are called BBOMLP and PSOMLP. We compared the performance of these three models with Wisconsin Breast Cancer dataset. Our simulation results show that the unsupervised model could achieve better performance than the implemented supervised models.