Affiliation:
1. Guru Nanak Dev University
Abstract
Abstract
The early detection of breast cancer is critical as it is the major cause of cancer in women. With an increase in population, the risk of death from breast cancer is also increasing, so, there is a need for a system that can automatically detect disease and aids medical health workers. The chapter contrasts the use of Machine Learning (ML) algorithms with two benchmark datasets, Wisconsin and the Coimbra datasets used to test the algorithms. The algorithm’s output is evaluated in terms of accuracy, precision, and recall. These techniques also provide the ROC and Area Under the Curve (AUC). According to the results, ANN beats SVM for both datasets giving an accuracy of 97.37% and 75% respectively.
Publisher
Research Square Platform LLC
Reference19 articles.
1. Comparative analysis of breast cancer detection using machine learning and biosensors;Amethiya Y;Intelligent Medicine,2022
2. Amrane, M., Oukid, S., Gagaoua, I., & Ensarİ, T. (2018). Breast cancer classification using machine learning. 2018 Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting (EBBT), 1–4. https://doi.org/10.1109/EBBT.2018.8391453
3. Survey of Machine Learning Techniques in Medical Imaging;Arasi M;International Journal of Advanced Trends in Computer Science and Engineering,2019
4. Probabilistic neural network for breast cancer classification;Azar A;Neural Computing and Applications,2012
5. Azar, A., & Elsaid, S. (2013). Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Computing and Applications, 24. https://doi.org/10.1007/s00521-012-1324-4