Comparitive analysis of machine learning techniques for classification of arbovirus

This paper studies classification methods, comparing svm and Naïve´s Bayes analysis as applied to viral disease medical data mining. The objective of this study is to explore possibility of applying machine learning techniques such as SVM and Naïve Bayes algorithm for classification to predict the susceptibility for complex disease-Dengue. Both of these algorithms were chosen for their simple, amazing and accurate results. The proposed work is to experiment machine learning algorithms to the available arbovirus that is causing frequent recurrent epidemics. In this paper, we discuss the application of machine learning techniques that make a distinction between dengue and other feverish illnesses in the primary care setting and predict severe arboviral disease among population. By investigating the arboviral dataset from one of the largest outbreaks that affected India in recent times, we master the methodology and validate classification performance as a measurement of the salience for the discovered associations. The result of the comparison between the methods showed that SVM outperforms the Naïve Bayes in Dengue disease diagnosis.