Using Machine Learning Algorithms for Automatic Breast Cancer Diagnosis

Abstract

Using machine learning and soft computing methods, there has been various empirical researches addressing breast cancer. Various authors assert that their algorithms are the most efficient, user-friendly, or precise available. This research uses genetic programming and machine learning methods to build a system for determining if breast tissue is benign or cancerous.This research aimed to find the best way to train the algorithm to detect them. Here, we used genetic programming to determine the optimal feature set and parameters for our machine learning classifiers. The sensitivity, specificity, precision, accuracy, and roc curves were used to evaluate the suggested method's efficacy. This research demonstrates that by using genetic programming in conjunction with feature preprocessing techniques and classifier algorithms, the optimal model may be found automatically.