Predictive Analysis for the Arbovirus-Dengue using SVM Classification

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Author(s) A.Shameem Fathima | D.Manimeglai
Pages 521-527
Volume 2
Issue 3
Date March, 2012
Keywords Dengue, Machine learning, Support vector machine, Classification
Abstract

Data mining in biology and medicine is a core component of biomedical informatics, and one of the first intensive applications of computer science to this field. Today’s biomedical data mining appears more multifaceted with advances in knowledge discovery in databases as well as machine learning approaches. This paper explores the application of machine learning technique- SVM for the identification of one of the Arboviral disease – Dengue. This paper reports novel biological discovery through nontrivial data mining process by using existing computational techniques. The goal of the system is to support the collection, and retrieval of public health documents, data, learning objects, and tools. We have deployed this generic infrastructure to facilitate data integration and knowledge sharing in the domain of dengue, which is one of the most prevalent viral diseases. This paper proposed an effort to apply the svm classification with the Radial basis function to classify the viral data and the model exhibits highly precise prediction rate.

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