BREAST CANCER DETECTION SYSTEM

Abstract

Machine learning and soft computing methods have been used in several studies to treat breast cancer. Countless groups assert that their algorithms are superior to those of others in terms of speed, simplicity, or accuracy. This study develops a system that can correctly identify between benign and malignant breast tumours using machine learning techniques. The research's aim was to make the learning algorithm better. To select the ideal features and parameter values for machine learning classifiers in this scenario, we used regression. Accuracy, precision and recall were the performance metrics.