A New Approach of Optimizing Breast Cancer Diagnosis Through Genetic Algorithm-Based Feature Selection

Breast cancer presents a significant health challenge, necessitating intricate detection methods. This study introduces a specialized 1D-Convolutional Neural Network for precise breast cancer classification. We conduct experiments, comparing a baseline model with Genetic Algorithm-based feature selection. Achieving superior performance in precision, recall, and accuracy, along with enhanced AUC and PR curves, our findings significantly advance breast cancer prediction models. This study provides innovative solutions for improved diagnostic accuracy and disease control.