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Reseach Article

Exploring Support Vector Machines and Random Forests for the Prognostic Study of an Arboviral Disease

by A. Shameem Fathima, L. Abdul Kadhar Sheriff
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 57 - Number 9
Year of Publication: 2012
Authors: A. Shameem Fathima, L. Abdul Kadhar Sheriff
10.5120/9140-3360

A. Shameem Fathima, L. Abdul Kadhar Sheriff . Exploring Support Vector Machines and Random Forests for the Prognostic Study of an Arboviral Disease. International Journal of Computer Applications. 57, 9 ( November 2012), 6-10. DOI=10.5120/9140-3360

@article{ 10.5120/9140-3360,
author = { A. Shameem Fathima, L. Abdul Kadhar Sheriff },
title = { Exploring Support Vector Machines and Random Forests for the Prognostic Study of an Arboviral Disease },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 57 },
number = { 9 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 6-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume57/number9/9140-3360/ },
doi = { 10.5120/9140-3360 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:59:58.523921+05:30
%A A. Shameem Fathima
%A L. Abdul Kadhar Sheriff
%T Exploring Support Vector Machines and Random Forests for the Prognostic Study of an Arboviral Disease
%J International Journal of Computer Applications
%@ 0975-8887
%V 57
%N 9
%P 6-10
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The need for rapid access to information in order to support critical decisions in public health cannot be disputed; however, development of such systems requires an understanding of the actual informational requirements of the practitioners. This paper explores the application of machine learning techniques for the detection of one of the Arboviral disease – Dengue. This paper reports original biological discovery through nontrivial data mining process by using accessible computational techniques. The goal of the system is to prop up the assortment, and recovery of public health documents, data, learning objects, and tools. We have deployed this standard infrastructure to facilitate data integration and knowledge sharing in the domain of dengue, which is one of the most prevalent Arboviral diseases. The proposed novel technique exhibits highly precise prediction rate (with total Mean Squared Error 0. 06665807).

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Index Terms

Computer Science
Information Sciences

Keywords

Dengue fever Data mining Machine learning techniques Support Vector Machine (SVM) Random Forest (RF) Feature Reduction