A Research on Breast Cancer Prediction using Data Mining Techniques
R. Preetha1, S. Vinila JinnyT2
1R. Preetha, Research Scholar, Department of Computer Science and Engineering, Noorul Islam Center for Higher Education, Kumaracoil (Tamil Nadu), India.
2S. Vinila Jinny, Associate Professor, Department of Computer Science and Engineering, Noorul Islam Center for Higher Education, Kumaracoil (Tamil Nadu), India.
Manuscript received on 06 September 2019 | Revised Manuscript received on 15 September 2019 | Manuscript Published on 26 October 2019 | PP: 362-370 | Volume-8 Issue-11S2 September 2019 | Retrieval Number: K105809811S219/2019©BEIESP | DOI: 10.35940/ijitee.K1058.09811S219
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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: Early detection and diagnosis of breast cancer plays a significant role in the welfare of women. The mortality rate due to breast cancer is on an all-time high. Factors such as food habits, environmental pollution, hectic lifestyle and genetics are commonly attributed to breast cancer. In order to detect and diagnose such types of cancer, intelligent systems are implemented. Automated diagnosis gets impacted by prediction accuracy when compared with surgical biopsy. Bioinformatics mining has emerged as the area of research that involves analyzing both data mining and Bioinformatics. In order to statistically find significant associations on a breast cancer data set, the result is conceivable. Using a larger data set results in discovering the correlations between a bigger set of gene. The algorithm has to be improved to perceive the interactions with low marginal. This research field affords most intelligent and reliable data mining models in breast cancer prediction and decision making. This survey reviews various data mining algorithms on large breast cancer biological datasets. The merits and demerits of various procedures and comparison of their corresponding results are presented in this work.
Keywords: Diagnosis, Breast Cancer, Data Mining Models.
Scope of the Article: Data Mining