Breast Cancer Identification Using Machine Learning

• Published in: Mathematical problems in engineering 2022-10, Vol.2022, p.1-8
• Main Authors: Jia, Xiao, Sun, Xiaolin, Zhang, Xingang
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial intelligence; Breast cancer; Datasets; Diagnosis; Disease; Engineering; Experiments; Machine learning; Mammography; Medical research; Methods; Morphology; Optimization; Optimization algorithms; Patients; Recognition; Researchers; Rural areas; Support vector machines; Tumors; Ultrasonic imaging; Womens health
• ISSN: 1024-123X; 1563-5147
• DOI: 10.1155/2022/8122895

Breast cancer is a cancer disease that seriously threatens women’s health and occupies the first place in female cancer mortality. At present, the incidence rate of breast cancer in China is the first in the world and is on the rise. In view of the serious harm of breast cancer to life and health, researchers and institutions are making unremitting efforts to find a perfect diagnosis and treatment plan. With the improvement of computer performance and machine learning levels, intelligent algorithms have been able to replace human behavior and judgment in some fields. The traditional breast cancer diagnosis process requires medical experts to observe patient data repeatedly. In this case, the algorithm technology is used to quickly feedback a high probability reference result to doctors, which is particularly important to increase the diagnosis efficiency and reduce the burden of doctors. In order to improve the accuracy of existing breast cancer recognition methods, this paper proposes and implements a scheme based on a whale optimization algorithm to iteratively adjust the key parameters of the support vector machine to improve the accuracy of breast cancer recognition. In order to verify the performance of the WOA-SVM algorithm, this paper uses the Wisconsin breast cancer data in the UCI database for performance verification experiments. Experiments show that the WOA-SVM model has higher recognition accuracy than the traditional breast cancer recognition model.