Study on Prediction Model of HIV Incidence Based on GRU Neural Network Optimized by MHPSO

被引：11

作者：

Li, Xiaoming ^{[1
,2
]}

Xu, Xianghui ^{[3
]}

Wang, Jie ^{[1
]}

Li, Jing ^{[1
]}

Qin, Sheng ^{[1
]}

Yuan, Juxiang ^{[1
]}

机构：

[1] North China Univ Sci & Technol, Sch Publ Hlth, Tangshan 063210, Peoples R China

[2] North China Univ Sci & Technol, Hebei Prov Key Lab Occupat Hlth & Safety Coal Ind, Tangshan 063210, Peoples R China

[3] North China Univ Sci & Technol, Dept Internal Med, Tangshan 063210, Peoples R China

来源：

IEEE ACCESS | 2020年 / 8卷

关键词：

Logic gates; Acquired immune deficiency syndrome; Neural networks; Human immunodeficiency virus; Computational modeling; Predictive models; Machine learning; AIDS; deep learning; incidence prediction; LSTM network; MHPSO-GRU network; RNN; HIV/AIDS; PREVALENCE; CHINA;

D O I：

10.1109/ACCESS.2020.2979859

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Acquired Immune Deficiency Syndrome (AIDS) is still one of the most life-threatening diseases in the world. Moreover, new infections are still potentially increasing. This difficult problem must be solved. Early warning is the most effective way to solve this problem. Here, we aim to determine the best performing model to track the epidemic of AIDS, which will provide a methodological basis for testing the time characteristics of the disease. From January 2004 to January 2018, we built four computing methods based on AIDS dataset: BPNN model, RNN model, LSTM model and MHPSO-GRU model. Compare the final estimated performance to determine the preferred method. Result. Considering the root mean square error (RMSE), mean absolute error (MAE), mean error rate (MER) and mean absolute percentage error (MAPE) in the simulation and prediction subsets, the MHPSO-GRU model is determined as the best performance technology. Estimates for the period from May 2018 to December 2020 suggest that the event appears to continue to increase and remain high.

引用

页码：49574 / 49583

页数：10

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