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.
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.
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在类似的气候和地理区域中，使用支撑载体机和埃及伊蚊感染率的登革热出血热（DHF）的发病率预测。
DOI：
10.1371/journal.pone.0125049
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Siriyasatien P
中科院分区：
文献类型：
--
作者：
Kesorn K;Ongruk P;Chompoosri J;Phumee A;Thavara U;Tawatsin A;Siriyasatien P
在过去的几十年里，一些研究人员提出了高度准确的预测模型，这些模型通常依赖于气候参数。然而，当气候因素应用于气候因素差异不显着的地区时，气候因素可能不可靠，并且会降低预测的有效性。本研究的目的是通过利用埃及伊蚊的感染率并使用支持向量机（SVM）技术预测登革热发病率，改进气候相似地区的登革热监测系统。对泰国中部登革热疫情高发地区进行了研究。所提出的框架由以下三个主要部分组成：1）数据集成，2）模型构建，3）模型评估。我们发现 Ae.埃及伊蚊雌蚊和幼蚊感染率与发病率呈显着正相关。因此，雌性蚊子和幼虫感染率的增加导致登革热病例数量增加，当将这些预测因子整合到预测模型中时，预测性能会提高。在这项研究中，我们应用带有径向基函数（RBF）核的支持向量机来预测高发病率，并采取预防措施来防止登革热流行的蔓延。实验结果表明，引入的参数在测试集数据上使用时，预测精度显着提高到 88.37%，并且与最先进的预测模型相比，这些参数带来了最高的性能。 Ae的感染率。埃及雌性蚊子和幼虫比经典框架中使用的气候参数更好地提高了发病率预测效率。我们证明，基于 SVM-R 的模型具有很高的泛化性能，并且通过准确性、灵敏度、特异性和平均绝对误差 (MAE) 来衡量，与经典模型相比，获得了最高的预测性能。
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. 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. 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).
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DOI：
10.1080/09603120801966043
发表时间：
2008-01-01
影响因子：
3.2
作者：
Halide, Halmar;Ridd, Peter
通讯作者：
Ridd, Peter
影响因子：
3.2
作者：
Ibrahim, F.;Faisal, T.;Taib, M. N.
通讯作者：
Taib, M. N.
影响因子：
3.7
作者：
Huang X;Williams G;Clements AC;Hu W
通讯作者：
Hu W
影响因子：
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作者：
Cao, LJ;Tay, FEH
通讯作者：
Tay, FEH
DOI：
10.1007/s11524-007-9170-x
发表时间：
2007-05
影响因子：
6.6
作者：
Campbell-Lendrum, Diarmid;Corvalan, Carlos
通讯作者：
Corvalan, Carlos