Automatic Prediction System of Dengue Haemorrhagic-Fever Outbreak Risk by Using Entropy and Artificial Neural Network

N. Rachata, P. Charoenkwan, T. Yooyativong, K. Chamnongthal, C. Lursinsap, K. Higuchi
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引用次数: 31

Abstract

Predicting Dengue Haemorrhagic Fever outbreak is obviously urgent in order to control and prevent a widespread of the fever in advance. However, the prediction of Dengue Haemorrhagic Fever outbreak needs the analysis from experts which is inconvenient and costly. An automatic prediction system should be developed. This paper proposes an automatic prediction system of Dengue Haemorrhagic-Fever outbreak risk by using entropy technique and artificial neural network. In this system, the information extraction is preprocessed prior to the prediction in order to reduce data redundancy and retain only those relevant data. First, the external factors such as temperature, relative humidity, and rainfall are considered during the information extraction. Then, a supervised neural network is deployed to predict the possible risk of Dengue Haemorrhagic Fever outbreak. To evaluate the performance of proposed system, the experiments based on the condition of weather data and Dengue Haemorrhagic Fever cases from January 1999 until December 2007 were conducted. Our prediction achieves 85.92% accuracy compared to the actual data.
基于熵和人工神经网络的登革出血热暴发风险自动预测系统
预测登革出血热暴发显然是迫切的,以便提前控制和防止该热的广泛传播。然而,登革出血热疫情的预测需要专家的分析,这既不方便又昂贵。应开发一种自动预报系统。本文提出了一种基于熵技术和人工神经网络的登革出血热暴发风险自动预测系统。该系统在预测前对信息提取进行预处理,以减少数据冗余,只保留相关数据。首先,在信息提取过程中考虑了温度、相对湿度、降雨量等外部因素。然后,利用监督神经网络预测登革出血热爆发的可能风险。为了评估系统的性能,以1999年1月至2007年12月的天气数据和登革热出血热病例为基础进行了实验。与实际数据相比,我们的预测准确率达到85.92%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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