Machine Learning Approach for Breast Cancer Prediction: A Review

Abstract

Breast cancer is a complicated and diverse illness that affects millions of women worldwide. A correct diagnosis and early detection are essential for effective therapy and better patient outcomes. In the past few years, developing predictive models and machine learning algorithms has received a lot of interest in the detection and diagnosis of breast cancer. This research study intends to present a thorough overview of the most recent breast cancer prognostic models, covering risk assessment, diagnosis, and prognosis. This paper addresses many different data types, including clinical, genetic, and imaging data, used in breast cancer prediction, as well as the several machine learning techniques used, including SVM, naïve Bayes, and random forests. A comparative analysis of different algorithms with methodology has been provided in this research study.