Exploring the Machine Learning Techniques in Early Detection of Breast Cancer

Abstract

Women frequently get breast cancer, and early detection is key to improving patient outcomes. Recently, machine learning techniques have showed promise in improving the accuracy and efficacy of breast cancer diagnosis. In this study, we analyze various machine learning techniques, such as logistic regression, decision trees, random forests, support vector machines, artificial neural networks, and deep learning, and its use in the early identification of breast cancer. We look at the challenges of applying these techniques and highlight the importance of large datasets for creating and testing machine learning models. We also discuss conventional methods for detecting breast cancer and its limitations, highlighting the promise of machine learning technologies to move past these limitations. Our results suggest that machine learning techniques might improve the accuracy of breast cancer detection and aid in early diagnosis, leading to better patient outcomes.