An Efficient System for Early Diagnosis of Breast Cancer using Support Vector Machine
Ahatsham, Anupam Singh1, Vivek Shahare2, Nitin Arora3
1Ahatsham, Department of Computer Science, University of Petroleum & Energy Studies, Dehradun, India.
2Anupam Singh, Department of Computer Science, University of Petroleum & Energy Studies, Dehradun, India.
3Vivek Shahare, Department of Computer Science, University of Petroleum & Energy Studies, Dehradun, India.
4Nitin Arora, Department of Computer Science, University of Petroleum & Energy Studies, Dehradun, India.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 7029-7035 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1626109119/2019©BEIESP | DOI: 10.35940/ijeat.A1626.109119
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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: There are many lives lost every year due to cancer and among them; among the women breast cancer causes the most deaths. For the better prediction of breast cancer risks, numerous studies have been undertaken incorporating data mining techniques. 1.1 million Cases of breast cancer were reported in 2004. It has been seen over the years that, that the numbers increase with the increasing industrialization and urbanization. It was earlier observed that mostly affected countries with breast cancer were high income countries such as America but now a days it is also very serious issue in middle and low income countries like Africa, Latin America and Asia. The main objective of this paper is to create a model which can more efficiently and accurately categorize a cancer as malignant or benevolent based on interpretation of the numerical values of attributes of ultrasound images of breast cancer. In this paper various data mining algorithm used like SVM(Support Vector Machine) for prediction and compared it with various other algorithms such as CART, Logistic Regression, KNN for the best training and test accuracy. SVM algorithm gives the most accurate results among the rest algorithm.
Keywords: Predictive Analysis, SVM, Breast Cancer, KNN, CART, Logistic Regression.