An Efficient Data Mining Techniques – Multi-Objective KNN Algorithm to Predict Breast Cancer
T. Mohana Priya1, M. Punithavalli2
1T. Mohana Priya, Research Scholar, Assistant Professor, Bharathiar University, Coimbatore, Dr. SNS Rajalakshmi College of Arts and Science Autonomous, Coimbatore (Tamil Nadu), India.
2Dr. M. Punithavalli, Associate Professor, Department of Computer Applications, Bharathiar University, Coimbatore (Tamil Nadu), India.
Manuscript received on 18 August 2019 | Revised Manuscript received on 09 September 2019 | Manuscript Published on 17 September 2019 | PP: 986-990 | Volume-8 Issue-2S8 August 2019 | Retrieval Number: B11880882S819/2019©BEIESP | DOI: 10.35940/ijrte.B1188.0882S819
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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 becomes most important foundation of mortality among women. The convenience of medical related dataset and data investigation support to extracting unidentified pattern in medical related or health related dataset. The objective of this research work is is to develop a health care prediction tool predicts the occurrence of the disease at near the beginning level of the criteria by analyzing the collected data set attributes to extract the disease exact level from the medical related information. The projected multi-objective KNN machine learning algorithm (classification) confirms that the highest accuracy (97.16%) is achieved compared to existing decision tree and Random Forest Techniques.
Keywords: Breast Cancer, Risk Prediction, Genetic Factors, Hormone Receptor Status.
Scope of the Article: Data Mining