An Efficient Cancer Prediction System using Ensemble Methods
Sopna P1, Sowmiya E2, Sanjana M3, Sujatha R4
1Sopna P*, Department of Computer Science & Engineering,Sri Krishna College of Technology, Coimbatore, India.
2Sowmiya E, Department of Computer Science & Engineering,Sri Krishna College of Technology, Coimbatore, India.
3Sanjana M, Department of Computer Science & Engineering,Sri Krishna College of Technology, Coimbatore, India.
4Sujatha R, Department of Computer Science & Engineering, Sri Krishna College of Technology,Coimbatore, India.
Manuscript received on April 18, 2020. | Revised Manuscript received on July 22, 2020. | Manuscript published on April 30, 2020. | PP: 627-630 | Volume-9 Issue-4, April 2020. | Retrieval Number: D7682049420/2020©BEIESP | DOI: 10.35940/ijeat.D7628.049420
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© The Authors. 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: Breast cancer is the most dreadful disease in the world in past few decades. Many women in the world has been affected due to this horrible disease and died. Breast cancer occurs in breast cells, the fatty tissue or the fibrous connective tissue in the breast. Breast cancer is malignant tumors tend to become progressively worse leading to death. Factors such as age genetic mutations and a family’s reordered history in breast cancer can increase the risk of breast cancer. Two types of tumors: Benign: this tumor type is not dangerous for a human body and rarely causes human death. Malignant: this tumor type is more dangerous and causes human death, it is called breast cancer. Machine learning was the boon technique in the fields of the medical industry. By the development of machine learning and data analytics a decision making tool can be made which helps in early detection and diagnosis of cancer tumor in women. This concept is to study and develop a decision based tool to eradicate breast cancer. The prediction system makes use of the ensemble algorithms to detect the cancer at earlier stage. It also differentiates the type of cancer from which the patient is being affected with effective accuracy.
Keywords: Decision Support Tools, Machine Learning, Prognosis, Diagnosis.