Breast Cancer Prediction using Machine Learning 
Sivapriya J1, Aravind Kumar V2, Siddarth Sai S3, Sriram S4
1Sivapriya J M.E- Assistant Professor in CSE at SRMIST, Ramapuram, Chennai.
2Sriram S- Student at SRMIST, Ramapuram, Chennai, India.
3Aravind Kumar V- Student at SRMIST, Ramapuram, Chennai , India.
4Siddarth Sai S- Student at SRMIST, Ramapuram, Chennai, India.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 25, 2019. | Manuscript published on 30 November, 2019. | PP: 4879-4881 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8292118419/2019©BEIESP | DOI: 10.35940/ijrte.D8292.118419
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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: One of the most dreadful disease is breast cancer and it has a potential cause for death in women. Every year, death rate increases drastically due to breast cancer. An effective way to classify data is through classification or data mining. This becomes very handy, especially in the medical field where diagnosis and analysis are done through these techniques. Wisconsin Breast cancer dataset is used to perform a comparison between SVM, Logistic Regression, Naïve Bayes and Random Forest. Evaluating the correctness in classifying data based on accuracy and time consumption is used to determine the efficiency of the algorithms, which is the main objective. Based on the result of performed experiments, the Random Forest algorithm shows the highest accuracy (99.76%) with the least error rate. ANACONDA Data Science Platform is used to execute all the experiments in a simulated environment.
Keywords: Accuracy, Algorithm, Breast Cancer.
Scope of the Article: Machine Learning.