Abstract:

Breast cancer remains a significant global health concern, with early detection playing a pivotal role in improving patient outcomes. Machine learning (ML) has emerged as a powerful tool in the field of medical research, offering the potential to enhance breast cancer prediction and diagnosis. This abstract provides an overview of the state-of-the-art techniques and recent advancements in utilizing ML for breast cancer prediction. This study highlights the importance of feature selection and extraction methods in optimizing the performance of ML models. Diverse datasets encompassing clinical, genomic, and radiomic data have been leveraged to train robust ML models. The abstract explores the various ML algorithms commonly employed for breast cancer prediction, such as logistic regression, support vector machines, random forests, and deep learning neural networks.

Description:

Breast Cancer, Algorithm, Network, Cancer, Machine