Machine Learning Algorithms to Improve the Performance Metrics of Breast Cancer Diagnosis

Cancer is the common problem for all people in the world with all types. Particularly, Breast Cancer is the most frequent disease as a cancer type for women. Therefore, any development for diagnosis and prediction of cancer disease is capital important for a healthy life. Cancer is a term for diseases in which abnormal cells divide without control and can invade nearby tissues. Cancer cells can also spread to other parts of the body through the blood and lymph systems. so, detecting the cancer in early stages is important for diagnosis. There are several main types of cancer. Carcinoma is a cancer that begins in the skin or in tissues that line or cover internal organs. Breast cancer starts when cells in the breast begin to grow out of control. These cells usually form a tumor that can often be seen on an x-ray or felt as a lump. Machine learning techniques can make a huge contribute on the process of early diagnosis and prediction of cancer. In this project I am mainly focusing on breast cancer. Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. The classification performance of these techniques has been compared with each other using the values of accuracy, precision, recall and ROC Area. The best performance has been obtained by Support Vector Machine technique with the highest accuracy. Citation: Dr. V. S. R. Kumari, Suresh Veesa, Srinivasa Rao Chevala. "Machine Learning Algorithms to Improve the Performance Metrics of Breast Cancer Diagnosis ." Global Research and Development Journal For Engineering 6.1 (2020): 8 - 11.