Dengue Risk Mapping from Geospatial Data Using GIS and Data Mining Techniques

Authors

  • Benjawan Hnusuwan
  • Siriwan Kajornkasirat
  • Supattra Puttinaovarat 1Faculty of Science and Industrial Technology, Prince of Songkla University, Surat Thani campus, Thailand

DOI:

https://doi.org/10.3991/ijoe.v16i11.16455

Keywords:

Dengue risk mapping, Geospatial data, Data mining, GIS

Abstract


Dengue fever is a major public health problem and has been an epidemic in Thailand for a long time. Therefore, there is a need to find a way to prevent the disease. This research aimed to explore the important factors of dengue fever, to study the factors affecting dengue hemorrhagic fever in Surat Thani Province, and to map the potential outbreak of dengue fever. Collecting patient information was done including, Rainfall, Digital Elevation Model (DEM), Land Use and Land Cover (LULC), Population Density, and Patients in Surat Thani Province, which was analyzed using data mining techniques involving analysis using 3 algorithms comprising Random Forest, J48, and Random Tree. The correct result is Random Forest since the accuracy of the data is 96.7 percent followed by J48 with accuracy of 95.9 percent. The final sequence is Random Tree with accuracy of 93.5 percent. Then, using the information can be displayed through ArcGIS program to see the risk points that are compared to the risk areas that have been previously done. The results can be very risky in Mueang District, Kanchanadit District, and Don Sak District, corresponding to the information obtained from the Public Health Office and the risk map created from the patient information.

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Published

2020-10-05

How to Cite

Hnusuwan, B., Kajornkasirat, S., & Puttinaovarat, S. (2020). Dengue Risk Mapping from Geospatial Data Using GIS and Data Mining Techniques. International Journal of Online and Biomedical Engineering (iJOE), 16(11), pp. 57–79. https://doi.org/10.3991/ijoe.v16i11.16455

Issue

Vol. 16 No. 11 (2020)

Section

Papers

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