Prediction and Classification into Benign and Malignant using the Clinical Testing Features
Olawale Adepoju1, Devaraj Verma C2

1Olawale Adepoju*, Department of Computer Science and Engineering, Jain Deemed to be University, Bangalore, India.
2Dr. Devaraj Verma C, Associate Professor, Department of Computer Science and Engineering, Jain Deemed to be University, Bangalore, India.
Manuscript received on July 15, 2020. | Revised Manuscript received on July 23, 2020. | Manuscript published on August 10, 2020. | PP: 55-61 | Volume-9 Issue-10, August 2020 | Retrieval Number: 100.1/ijitee.J74110891020 | DOI: 10.35940/ijitee.J7411.0891020
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Abstract: Breast Cancer is the most often identified cancer among women and a major reason for the increased 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. The advanced engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. Data mining techniques contribute a lot to the development of such a system, Classification, and data mining methods are an effective way to classify data. For the classification of benign and malignant tumors, we have used classification techniques of machine learning in which the machine learns from the past data and can predict the category of new input. This study is a relative study on the implementation of models using Support Vector Machine (SVM), and Naïve Bayes on Breast cancer Wisconsin (Original) Data Set. With respect to the results of accuracy, precision, sensitivity, specificity, error rate, and f1 score, the efficiency of each algorithm is measured and compared. Our experiments have shown that SVM is the best for predictive analysis with an accuracy of 99.28% and naïve Bayes with an accuracy of 98.56%. It is inferred from this study that SVM is the well-suited algorithm for prediction. 
Keywords: Breast Cancer, Data Mining, Machine Learning, Naïve Bayes, Support Vector Machine.
Scope of the Article: Classification