Survival Outcome Prediction for Breast Cancer Patients
Dhivya S1, Arulprabha R2, Kowsalya M3, Gaddam Hemanth Kumar4, Mullagiri Bhavan Premchand Gandhi5
1Dhivya S, Assistant Professor, Department Of Computer Science and Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, India.
2Arulprabha R, Department Of Computer Science and Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, India.
3Kowsalya M, Department Of Computer Science and Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, India.
4Gaddam Hemanth Kumar, Department Of Computer Science and Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, India.
5Mullagiri Bhavan Premchand Gandhi, Department Of Computer Science and Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, India.
Manuscript received on April 30, 2020. | Revised Manuscript received on May 06, 2020. | Manuscript published on May 30, 2020. | PP: 1589-1592 | Volume-9 Issue-1, May 2020. | Retrieval Number: A1592059120/2020©BEIESP | DOI: 10.35940/ijrte.A1592.059120
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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: The second most causative disease is breast cancer happening in women and a significant explanation behind expanding death rate among women. Observed rates of this cancer are increasing with industrialization and also with early detection facilities. As the finding of this ailment physically takes extended periods and the lesser accessibility of frameworks, there is a need to build up the programmed determination framework for early identification of malignant growth. We have used machine learning classification techniques to categorize benign and malignant tumors, in which the machine learns from past data and predicts the new input category. Models like logistic regression and Random Forest are Done on the UCI dataset. Our experiments have indicated that Random Forest has the best prescient examination with exactness of ~96%.
Keywords: Logistic Regression, Random Forest, Decision Tree
Scope of the Article: Decision Making