A Support Vector Machine and Decision Tree Based Breast Cancer Prediction
Tsehay Admassu Assegie1, Sushma S. J2

1Tsehay Admassu Assegie*, department of computing technology, college of engineering and technology, Aksum University, Aksum, Ethiopia.
2Sushma S. J., Associate Professor, GSSSIETW, Mysuru, Karnataka, India.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2972-2976 | Volume-9 Issue-3, February 2020. | Retrieval Number:  A1752109119/2020©BEIESP | DOI: 10.35940/ijeat.A1752.029320
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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: The first step in diagnosis of a breast cancer is the identification of the disease. Early detection of the breast cancer is significant to reduce the mortality rate due to breast cancer. Machine learning algorithms can be used in identification of the breast cancer. The supervised machine learning algorithms such as Support Vector Machine (SVM) and the Decision Tree are widely used in classification problems, such as the identification of breast cancer. In this study, a machine learning model is proposed by employing learning algorithms namely, the support vector machine and decision tree. The kaggle data repository consisting of 569 observations of malignant and benign observations is used to develop the proposed model. Finally, the model is evaluated using accuracy, confusion matrix precision and recall as metrics for evaluation of performance on the test set. The analysis result showed that, the support vector machine (SVM) has better accuracy and less number of misclassification rate and better precision than the decision tree algorithm. The average accuracy of the support vector machine (SVM) is 91.92 % and that of the decision tree classification model is 87.12 %.
Keywords: Breast cancer diagnosis, decision tree classification, SVM classification, machine learning.