Intelligent System for Predicting and Diagnosis of Breast Cancer
Saeed Khodary M. Hamouda1, Reda. H. Abo El-Ezz2, Mohammed E. Wahed3
1Saeed Khodary M. Hamouda, Research Scholar, Computer Laboratories, Zagazig University, Zagazig, Egypt.
2Reda. H. Abo El-Ezz, Prof. at Computer& System Dept., Faculty of Engineering, Al-Azhar University, Cairo, Egypt.
3Mohammed E. Wahed, Prof. at Faculty Of Computers and Informatics, Suez Canal University, Ismailia, Egypt.
Manuscript received on February 06, 2017. | Revised Manuscript received on February 15, 2017. | Manuscript published on February 25, 2017. | PP: 29-35 | Volume-4 Issue-8, February 2017. | Retrieval Number: H1151024817
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cancer is a disease in which cells in the body grow out of control. When cancer starts in the breast, it is called breast cancer. Breast cancer is one of the major death causing diseases of the women in the world. Every year more than million women are diagnosed with breast cancer more than half of them will die because of inaccuracies and delays in diagnosis of the disease. High accuracy in cancer prediction is important to improve the treatment quality and the survivability rate of patients. This paper, presents a Rough Set Theory (RST) as an efficient and intelligent technique, to analyze breast cancer dataset, approximate set classification, and improve the accuracy of diagnosis with limited attributes. (RST) estimates the risk of the breast cancer at the earlier stage, and may help researchers and consultants for predicting and diagnosis of breast cancer early.
Keywords: Breast Cancer, Diagnosis, MATLAB, Predictive, Rough Set Theory, Survivability.