Artificial Intelligence Models for Breast Cancer Prediction: A Comprehensive Review

Abstract

Breast cancer (BC) caused death in the worldwide, which raises the issue of the urgent essential for accurate and primary prediction. In oncology, we are seeing the rapid integration of Artificial Intelligence (AI), inclusive machine learning and deep learning, and we view this as a transformative opportunity to improve predictive analytics. This review paper looks at the present state of artificial intelligence (AI) in the prediction of breast cancer, which is the most common of all the malignancies worldwide. We examine the synthesis and analysis of recent advances, methods, and performance results for many AI models, inclusive ML and DL models. DL architectures, in particular Convolutional Neural Networks (CNNs), have achieved the best results in image-based detection tasks. Furthermore, the paper highlights that what may be the here and now in terms of performance does not diminish the value and interpretability of traditional ML models used for structured clinical and genomic data.