Editors: Dr Mukul Kumar Baruah, Dr Rahul Kanti Nath & Dr Joyobrato Nath (2025)

ISBN: 97978-93-94174-62-7

Application of Machine Learning Techniques for Breast Cancer Detection: A Review

Department of Computer Science and Information Technology, Cotton University, Guwahati, India

Department of Zoology, M.H.C.M Science College, Algapur, Hailakandi, India

Breast cancer remains a major worldwide public health challenge because it ranks as one of the top causes of women's cancer deaths. Early detection plays a crucial role in improving both survival rates and patient prognosis for cancer patients. Machine learning (ML) has evolved quickly into an essential computational resource for breast cancer diagnosis and screening because of its transformative power in this medical field. The review article investigates different ML techniques which have been developed to detect breast cancer and their various applications. The study examines numerous machine learning approaches from classic algorithms including Support Vector Machines and Random Forests to modern deep learning models like Convolution Neural Networks that demonstrate great potential in processing complex medical imaging data. This section examines the publicly available datasets which serve as essential resources for both training ML models and their validation while emphasizing their distinct characteristics and practical applications. The article includes a specific section that explores various feature selection techniques which are essential for identifying key diagnostic markers from large clinical and imaging datasets and for improving model effectiveness and reducing computational demands. The paper delivers a critical assessment of performance metrics and examines how different classification models compare in effectiveness within this field. The analysis concludes by exploring fundamental obstacles in the domain such as limited data availability, interpretation challenges and generalization problems while mapping out prospective research and practical application avenues to advance machine learning based diagnostic solutions.

*Corresponding author; e-mail: shamimbiotech2009@gmail.com

Cite this Chapter: Sangita Baruah and Shamim Ahmed Shamim Khan Barbhuiya. 2025. Application of Machine Learning Techniques for Breast Cancer Detection: A Review. In: Mukul Kumar Baruah, Rahul Kanti Nath and Joyobrato Nath (Eds.), Transdisciplinary Science: Mapping the Future of Research. Excellent Publishers, India. pp. 175-184. doi: https://doi.org/10.20546/978-93-94174-62-7_16