Study of Classification of Diseases by Genetic Algorithm for Multiclass Support Vector Machine Using Hadoop

Abstract

Abstract:Since many years ago, the scientific community is concerned about how to increase the accuracy of different classification methods, and major achievements have been made so far. Hadoop and MapReduce are used to handle these large volumes of variable size data. This work focuses on the combining a feature selection technique based on genetic algorithm and support vector machines (SVM) of medical disease classification. SVM is relatively a novel classification technique and has been shown higher performance than traditional learning methods in many applications. The idea is to use GA as an optimizer to find the optimal values of hyper-parameters of SVM and adopt a supervised learning approach to train the SVM model. In this paper we propose a genetic algorithm (GA) based classification method.