Disease Prediction using Big Data Analytics and SVM
V. Sahaya Sakila1, S. Sri Gayathri2
1Ms. V. Sahaya Sakila, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
2S. Sri Gayathri, Department of Computer Science and Engineering, 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: 755-759 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6263048419/19©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: The biomedical communities have been facing a rapid big data growth. Medical data can be effectively used in disease detection, treatment and cure. There is a need for proper and effective methods in order to analyse the data accurately. The frequency and the common kinds of diseases may vary from region to region but the characteristics of the diseases exhibited by different regions can be different, thus, making the prediction of disease outbreaks difficult. Here, machine learning algorithms are streamlined for effective prediction of diseases. The algorithm to be used is Support Vector Machine, also known as SVM. Support vector machines are models that come under supervised machine learning, and generally consist of algorithms that are used to analyse data for regression analysis after classification. That is, taking a set of training examples, each of it is segregated based on the category it belongs to among the two and a model is built by an SVM training algorithm that would assign examples to either of the two categories.
Keywords: Data Mining, Support Vector Machine, Medical Communities, Datasets
Scope of the Article: Data Mining