Machine Learning for Dengue Outbreak Prediction: A Performance Evaluation of Different Prominent Classifiers

被引：19

作者：

Iqbal, Naiyar ^{[1
]}

Islam, Mohammad ^{[1
]}

机构：

[1] Maulana Azad Natl Urdu Univ, Dept Comp Sci & Informat Technol, Hyderabad, Telangana, India

来源：

INFORMATICA-JOURNAL OF COMPUTING AND INFORMATICS | 2019年 / 43卷 / 03期

关键词：

Dengue fever; machine learning; classification; ensemble classifier; clinical symptoms; FEVER;

D O I：

10.31449/inf.v43i1.1548

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Dengue disease patients are increasing rapidly and actually dengue has recorded in every continent today according to the World Health Organization (WHO) record. By WHO report the number of dengue outbreak cases announced every year has expanded from 0.4 to 1.3 million during the period of 1996 to 2005 and then it has reached to 2.2 to 3.2 million during the year of 2010 to 2015 respectively. Consequently, it is fundamental to have a structure that can adequately perceive the pervasiveness of dengue outbreak in a large number of specimens momentarily. At this critical moment, the capability of seven prominent machine learning systems was assessed for the forecast of the dengue outbreak. These methods are evaluated by eight miscellaneous performance parameters. LogitBoost ensemble model is reported as the topmost classification accuracy of 92% with sensitivity and specificity of 90 and 94 % respectively.

引用

页码：363 / 371

页数：9

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