Disease prediction using big data analytics and SVM

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.