基于Elman Levenberg神经网络和遗传算法的登革出血热暴发预测

N. Saptarini, Rocky Yefrenes Dillak, P. Pakan
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引用次数: 3

摘要

登革出血热(DHF)是一种非常危险的疾病,威胁着人类的生存。这种疾病通常容易传播,并导致儿童死亡,特别是15岁以下的儿童。本研究的目的是开发一个系统,该系统可用于使用改进的Elman Levenberg-Marquardt神经网络算法准确预测印度尼西亚库邦市的登革出血热病例数。本研究使用4个输入变量作为DHF传播的影响因素,即:(i)平均温度,(ii)平均降雨量,(iii)湿度和(iv)海平面。根据检测结果,该模型预测DHF病例的RMSE为1384。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dengue Haemorrhagic Fever Outbreak Prediction using Elman Levenberg Neural Network and Genetic Algorithm
Dengue Hemorrhagic Fever (DHF) is a very dangerous disease and has threatened human survival. This disease usually spreads easily and causes of death in children especially those younger than 15 years. The purpose of this research is to develop a system that can be used to predict the number of DHF cases in the city of Kupang, Indonesia accurately using improved Elman Levenberg-Marquardt Neural Network algorithm. This study used four (4) input variables, which are the factors that contribute to the spread of DHF, namely: (i) The average temperature, (ii) The average of rainfall, (iii) The humidity and (iv) The sea level. Based on test results, the model can predict DHF cases with RMSE of 1,384.
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