คลังข้อมูลการวิจัยการเกษตรไทย

ผู้แต่ง Kraisak Kesorn

เผยแพร่โดย

1 Computer Science and Information Technology Department, Faculty of Science, Naresuan University, Phitsanulok, Thailand, 2 National Institute of Health, Department of Medical Sciences, Ministry of Public Health, Nonthaburi, Thailand, 3 Department of Parasitology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand, 4 Excellence Center for Emerging Infectious Disease, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand

เผยแพร่เมื่อ ไม่ระบุวันที่เผยแพร่

หน่วยงาน ไม่ระบุหน่วยงาน

งานวิจัยที่แนะนำ Climatic factors influencing dengue hemorrhagic fever in Kolaka district, Indonesia Induction of defensin response to dengue infection in Aedes aegypti Machine Learning Double Dengue Serotypes in Asymptomatic Populations Living in An Area of Thailand Endemic for Dengue Hemorrhagic Fever Comparative effectiveness of monomolecular surface film on Aedes aegypti (L.) and Anopheles minimus (Theobald) (Diptera: Culicidae) Evaluation of papaya ringspot virus as a vector for expression of dengue e protein domain III in Cucurbita pepo (Zucchini) plants Physical characteristics and reproductive performance in Aedes (Diptera: Culicidae) Synergistic effect of combination deltamethrin and piperonyl butoxide on behavioral avoidance and mortality responses of Aedes aegypti L. Larvicidal effect of compounds isolated from Maerua siamensis (Capparidaceae) against Aedes aegypti (Diptera: Culicidae) larvae Relationships between dengue virus infection in mosquito vector, (Aedes aegypti), dengue cases and weather conditions in Samut Sakhon Province, Thailand

คัดลอก URL

ชื่อเรื่อง (EN):	Morbidity Rate Prediction of Dengue Hemorrhagic Fever (DHF) Using the Support Vector Machine and the Aedes aegypti Infection Rate in Similar Climates and Geographical Areas
ผู้แต่ง / หัวหน้าโครงการ (EN):	Kraisak Kesorn
บทคัดย่อ (EN):	Background In the past few decades, several researchers have proposed highly accurate prediction models that have typically relied on climate parameters. However, climate factors can be unreliable and can lower the effectiveness of prediction when they are applied in locations where climate factors do not differ significantly. The purpose of this study was to improve a dengue surveillance system in areas with similar climate by exploiting the infection rate in the Aedes aegypti mosquito and using the support vector machine (SVM) technique for forecasting the dengue morbidity rate. Methods and Findings Areas with high incidence of dengue outbreaks in central Thailand were studied. The proposed framework consisted of the following three major parts: 1) data integration, 2) model construction, and 3) model evaluation. We discovered that the Ae. aegypti female and larvae mosquito infection rates were significantly positively associated with the morbidity rate. Thus, the increasing infection rate of female mosquitoes and larvae led to a higher number of dengue cases, and the prediction performance increased when those predictors were integrated into a predictive model. In this research, we applied the SVM with the radial basis function (RBF) kernel to forecast the high morbidity rate and take precautions to prevent the development of pervasive dengue epidemics. The experimental results showed that the introduced parameters significantly increased the prediction accuracy to 88.37% when used on the test set data, and these parameters led to the highest performance compared to state-of-the-art forecasting models. Conclusions The infection rates of the Ae. aegypti female mosquitoes and larvae improved the morbidity rate forecasting efficiency better than the climate parameters used in classical frameworks. We demonstrated that the SVM-R-based model has high generalization performance and obtained the highest prediction performance compared to classical models as measured by the accuracy, sensitivity, specificity, and mean absolute error (MAE).
บทคัดย่อ:	ไม่พบข้อมูลจากหน่วยงานต้นทาง
ภาษา (EN):	en
เอกสารแนบ (EN):	http://nih.dmsc.moph.go.th/research/showimgdetil.php?id=474
เผยแพร่โดย (EN):	1 Computer Science and Information Technology Department, Faculty of Science, Naresuan University, Phitsanulok, Thailand, 2 National Institute of Health, Department of Medical Sciences, Ministry of Public Health, Nonthaburi, Thailand, 3 Department of Parasitology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand, 4 Excellence Center for Emerging Infectious Disease, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
คำสำคัญ (EN):	(DHF)
เจ้าของลิขสิทธิ์ (EN):	National Institute of Health, Department of Medical Science