Main Article Content
Breast cancer is one type of cancer which causes from breast tissue. A lump in the breast, skin dimpling, breast shape changes, fluid from the nipple, or a red scaly patch of skin are some of signs of breast cancer. In the world, cancer is one of the most leading causes of deaths among the women. Among the cancer diseases, breast cancer is especially a concern in women. Mammography is one of the methods for finding tumor in the breast. This method is utilized to detect the cancer which is helpful for the doctor or radiologists. Due to the inexperience�s in the field of cancer detection, the abnormality is missed by doctor or Radiologists. Segmentation is very expensive for doctor and radiologists to examine the data in the mammogram. In mammogram the accuracy rate is based on the image segmentation. The recent clustering techniques are presented in this paper for detection of breast cancer. These Classification algorithms have been mostly studied which is applied in a various application areas. To maximize the efficiency of the searching process various clustering techniques are recommended. In this paper, we have presented a survey of Classification techniques.
How to Cite
, K. M. D. R. J. R. S. (2018). A Computational Approach to Predict the Severity of Breast Cancer through Machine Learning Algorithms. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 4(4), 529–535. Retrieved from https://ijfrcsce.org/index.php/ijfrcsce/article/view/1562