Turkish Journal of Physiotherapy and Rehabilitation; 32(3) ISSN 2651-4451 | e-ISSN 2651-446X RISK MAP OF DENGUE CASE IN THE SOUTHERNMOST PROVINCES OF THAILAND USING A MACHINE LEARNING Teerawad Sriklin 1, Supattra Puttinaovarat 2, Siriwan Kajornkasirat 3 1,2,3 Faculty of Science and Industrial Technology, Prince of Songkla University, Surat Thani Campus,Surat Thani 84000, Thailand Email:1
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[email protected] ABSTRACT This study aimed to compare a machine learning model suitable for spatial data on dengue cases in the three southernmost provinces of Thailand including Pattani, Yala, and Narathiwat provinces. Data on the dengue cases and weather data including rainfall, rainy day, temperature, relative humidity, and air pressure for the period of 2015 to 2019 were obtained from the Bureau of Epidemiology, and the Meteorological Department of Southern Thailand, respectively. The machine learning models used for testing to compare the accuracy of predictions were ANN, Random Forest, SVM, and J48. The results showed that the ANN model at the number of iterations 2500 rounds gives the highest accuracy (98.89%) with lowest root mean square error and mean absolute error. Narathiwat (Russo, Si Sakhon, and Chanae District) and Pattani (Yarang and Mayo District) provinces are defined to the high-risk areas. Yala province was the low-risk area corresponding to the information obtained from the Public Health Office and the risk map created from the patient information. Keywords:dengue, ANN, Random Forest, SVM, J48. I. INTRODUCTION The World Health Organization [1] revealed the rapid increase in the number of Dengue Hemorrhagic Fever (DHF) cases are caused by the dengue virus, which contagious disease caused by mosquitoes that reached 30 times during the last 50 year.