Comparative Study of SVM and KNN for Tumor Prediction
J. Sivapriya1, Nishanth Prem2, G. Venkatesh Prasad3, Balasubramanian C.L.4
1J. Sivapriya, Assistant Professor (O.G.) at Department of Computer Science Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
2Nishanth Prem, B.Tech. Student, Department of Computer Science Student at SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
3G. Venkatesh Prasad, B.Tech. Student, Department of Computer Science Student at SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
4Balasubramanian C.L., B.Tech. Student, Department of Computer Science, Student, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 602-605 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6646048419/19©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: Classification algorithms have played a vital role in the field of machine learning and data science. They cannot be downplayed. There are several variants of classification algorithms. In this paper, we compare KNN (K- nearest neighbors) and SVM (Support Vector Machine) algorithms. The attributes of both the algorithms are conferred. The benefits and drawbacks of each of these algorithms are assessed and finally arrive at a conclusion on which one has higher efficiency. We shall examine the efficiency of each algorithm based on their learning curve, comparing their accuracy on tumor prediction.
Keywords: Big Data, Healthcare, SVM Algorithm, Tumor Detection.
Scope of the Article: Healthcare Informatics