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基于遗传算法的优化神经网络构建手足口病预测预警模型。

Optimized Neural Network Based on Genetic Algorithm to Construct Hand-Foot-and-Mouth Disease Prediction and Early-Warning Model.

机构信息

School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China.

Chinese Center for Disease Control and Prevention, Beijing 102206, China.

出版信息

Int J Environ Res Public Health. 2021 Mar 14;18(6):2959. doi: 10.3390/ijerph18062959.

DOI:10.3390/ijerph18062959
PMID:33799332
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8001304/
Abstract

Accompanied by the rapid economic and social development, there is a phenomenon of the crazy spread of many infectious diseases. It has brought the rapid growth of the number of people infected with hand-foot-and-mouth disease (HFMD), and children, especially infants and young children's health is at great risk. So it is very important to predict the number of HFMD infections and realize the regional early-warning of HFMD based on big data. However, in the current field of infectious diseases, the research on the prevalence of HFMD mainly predicts the number of future cases based on the number of historical cases in various places, and the influence of many related factors that affect the prevalence of HFMD is ignored. The current early-warning research of HFMD mainly uses direct case report, which uses statistical methods in time and space to have early-warnings of outbreaks separately. It leads to a high error rate and low confidence in the early-warning results. This paper uses machine learning methods to establish a HFMD epidemic prediction model and explore constructing a variety of early-warning models. By comparison of experimental results, we finally verify that the HFMD prediction algorithm proposed in this paper has higher accuracy. At the same time, the early-warning algorithm based on the comparison of threshold has good results.

摘要

伴随着经济社会的快速发展，许多传染病呈现出疯狂传播的现象。这导致手足口病（HFMD）感染人数迅速增加，儿童，尤其是婴幼儿的健康受到极大威胁。因此，基于大数据预测手足口病感染人数并实现区域预警非常重要。然而，在当前传染病领域，对手足口病流行情况的研究主要是基于各地历史病例数量预测未来病例数量，忽略了许多影响手足口病流行的相关因素。目前手足口病的预警研究主要采用直接病例报告，利用时间和空间的统计方法分别对疫情爆发进行预警。这导致预警结果的误差率较高，置信度较低。本文使用机器学习方法建立了手足口病疫情预测模型，并探讨了构建多种预警模型。通过实验结果的比较，最终验证了本文提出的手足口病预测算法具有更高的准确性。同时，基于阈值比较的预警算法也取得了良好的效果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/59f8c1aa1878/ijerph-18-02959-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/9be12d397b0f/ijerph-18-02959-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/287ca761c822/ijerph-18-02959-g002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/a5d2255a6502/ijerph-18-02959-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/cac2683843a9/ijerph-18-02959-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/11a010dd57cb/ijerph-18-02959-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/63f3f7c7d445/ijerph-18-02959-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/144ab7c4488c/ijerph-18-02959-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/2726d0cc0858/ijerph-18-02959-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/89e673003471/ijerph-18-02959-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/d61818919610/ijerph-18-02959-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/63af863ce3f5/ijerph-18-02959-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/806e815a26e5/ijerph-18-02959-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/fb356c5f9717/ijerph-18-02959-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/266c0a590f92/ijerph-18-02959-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/59f8c1aa1878/ijerph-18-02959-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/9be12d397b0f/ijerph-18-02959-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/287ca761c822/ijerph-18-02959-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/3b9cf5aa7ee8/ijerph-18-02959-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/a5d2255a6502/ijerph-18-02959-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/cac2683843a9/ijerph-18-02959-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/11a010dd57cb/ijerph-18-02959-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/63f3f7c7d445/ijerph-18-02959-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/144ab7c4488c/ijerph-18-02959-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/2726d0cc0858/ijerph-18-02959-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/89e673003471/ijerph-18-02959-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/d61818919610/ijerph-18-02959-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/63af863ce3f5/ijerph-18-02959-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/806e815a26e5/ijerph-18-02959-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/fb356c5f9717/ijerph-18-02959-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/266c0a590f92/ijerph-18-02959-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4928/8001304/59f8c1aa1878/ijerph-18-02959-g016.jpg

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[Application of moving epidemic method in establishing epidemic intensity threshold of hand, foot, and mouth disease in southern China].移动流行趋势法在确定中国南方手足口病流行强度阈值中的应用
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Prediction of Myopia in Adolescents through Machine Learning Methods.
中国大陆手足口病发病率的混合模型预测研究。
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