Analysis of early diagnosis of breast cancer using support vector machine

Breast cancer, also known as breast cancer, is one of the most common types of cancer among women. With the beginning of this cancer, abnormal cells begin to grow without any particular rule, and in fact, this cancer begins in the breast tissue. The most important factor that reduces the chance of death in this disease is increasing awareness about this disease; Because the sooner breast cancer is diagnosed, the better the treatment will be and the success rate of the treatment will increase. Breast cancer cells usually form a tumor that is usually seen or felt as a mass by ultrasound or mammography. Most breast lumps are benign 1 and not cancerous or malignant. To determine whether the cancer is benign or malignant and whether it will affect the risk of developing cancer in the future, any cancerous mass or changes in the appearance of the breast should be examined by a doctor. This research has diagnosed breast cancer with the help of data mining in order to help doctors in medical science with the help of computer science and svm showed the highest accuracy with 100% accuracy. In this research, we have done this with the help of three techniques: svm , svm pso, and hyper hyper. In this study, the importance of native models in breast cancer diagnosis was investigated by preparing the dataset from the Shahid Motahari Clinic in Shiraz, and performing the mentioned operations on the samples.

Analysis of early diagnosis of breast cancer using support vector machine Keywords:

Breast cancer , diagnosis , support vector machine , data mining