Visualization of Dengue Incidences for Vulnerability using K-means

被引：0

作者：

Mathur, Nirbhay ^{[1
]}

Asirvadam, Vijanth S. ^{[1
]}

Dass, Sarat C. ^{[2
]}

Gill, Balvinder Singh ^{[3
]}

机构：

[1] Univ Teknol PETRONAS, CISIR, Dept Elect & Elect Engn, Bander Seri Iskandar 32610, Perak, Malaysia

[2] Univ Teknol PETRONAS, CISIR, Dept Fundamental & Appl Sci, Bander Seri Iskandar 32610, Perak, Malaysia

[3] Minist Hlth Malaysia MoH, Dis ControlDiv, Kuala Lumpur, Malaysia

来源：

2015 IEEE INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING APPLICATIONS (ICSIPA) | 2015年

关键词：

Dengue; Geographical Information System (GIS); K-means; clustering; Gaussian Mixture model; AEDES-ALBOPICTUS; ABUNDANCE; VARIABLES; PENANG; VECTOR; MODELS; FEVER;

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Dengue is the world's most rapidly spreading and geographically widespread arthropod-borne disease. Dengue epidemics are observed to be larger, more frequent and associated with more severe disease than they were in the past. To control the incidence of the disease, it is important to be able to identify the hot-spots localized regions of high incidences. This work focuses on identifying hot-spots of dengue using the K-means clustering algorithm. Data is collected from the state of Selangor in Malaysia from 2013 to 2014. Visualization of dengue vulnerability is obtained via Gaussian mixture models fitted using K-means algorithm. Results demonstrate the ability to render visualization for the vulnerability of dengue incidences on the basis of high density and low density cluster using Gaussian mixture and K-means algorithm.

引用

页码：569 / 573

页数：5

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