An evaluation of the effectiveness of machine learning prediction models in assessing breast cancer risk

Abstract

Breast cancer is a prevalent disease that has a potential influence on the lives of countless women globally. Early diagnosis and intervention are crucial for successful treatment and better patient outcomes. Machine learning algorithms have shown promising results in developing accurate and dependable prediction models for breast cancer. In this research, we conduct an extensive overview of various machine learning (ML) techniques employed to develop breast cancer prediction models using diverse datasets. Our study explores the literature on several ML algorithms utilized for breast cancer prediction. We also examine the types of datasets used for training and testing these models, including clinical data, mammography images, and genetic data. Additionally, we evaluate the benefits and limitations of each machine learning algorithm and dataset and offer recommendations for future research. Our aim is to provide a comprehensive understanding of the current state-of-the-art in breast cancer prediction models using ML and to promote the development of precise and effective models to detect breast cancer at an early stage.

Keywords

• Machine learning
• Prediction
• Breast cancer