Prediction of Breast Cancer Using Supervised Machine Learning Techniques
Ch. Shravya1, K. Pravalika2, Shaik Subhani3
1Kuthuru Pravalika, Department of Information Technology, Sreenidhi Institute of Science and Technology, Hyderabad (Telangana), India.
2Chakinam Shravya, Department of Information Technology, Sreenidhi Institute of Science and Technology, Hyderabad (Telangana), India.
3Dr. Shaik Subhani, Department of Information Technology, Sreenidhi Institute of Science and Technology, Hyderabad (Telangana), India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 1106-1110 | Volume-8 Issue-6, April 2019 | Retrieval Number: F3384048619/19©BEIESP
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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 often identified cancer among women and major reason for increasing mortality rate among women. As the diagnosis of this disease manually takes long hours and the lesser availability of systems, there is a need to develop the automatic diagnosis system for early detection of cancer. Data mining techniques contribute a lot in the development of such system. For the classification of benign and malignant tumor we have used classification techniques of machine learning in which the machine is learned from the past data and can predict the category of new input. This paper is a relative study on the implementation of models using Logistic Regression, Support Vector Machine (SVM) and K Nearest Neighbor (KNN) is done on the dataset taken from the UCI repository. With respect to the results of accuracy, precision, sensitivity, specificity and False Positive Rate the efficiency of each algorithm is measured and compared. These techniques are coded in python and executed in Spyder, the Scientific Python Development Environment. Our experiments have shown that SVM is the best for predictive analysis with an accuracy of 92.7%.We infer from our study that SVM is the well suited algorithm for prediction and on the whole KNN presented well next to SVM.
Keyword: Classification, Logistic Regression, KNN, SVM.
Scope of the Article: Machine Learning