Study on Prediction Model of HIV Incidence Based on GRU Neural Network Optimized by MHPSO
Study on Prediction Model of HIV Incidence Based on GRU Neural Network Optimized by MHPSO
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DOI：
10.1109/access.2020.2979859
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Yuan, Juxiang
中科院分区：
文献类型：
--
作者：
Li, Xiaoming;Xu, Xianghui;Yuan, Juxiang
获得性免疫缺陷综合症（艾滋病）仍然是世界上最威胁生命的疾病之一。此外，新感染病例仍有可能增加。这个难题必须得到解决。预警是解决这一问题最有效的方法。在这里，我们的目标是确定跟踪艾滋病流行情况的最佳模型，这将为测试该疾病的时间特征提供方法学基础。 2004年1月至2018年1月，我们基于艾滋病数据集构建了四种计算方法：BPNN模型、RNN模型、LSTM模型和MHPSO-GRU模型。比较最终的估计性能以确定首选方法。结果。考虑到仿真和预测子集中的均方根误差（RMSE）、平均绝对误差（MAE）、平均误差率（MER）和平均绝对百分比误差（MAPE），MHPSO-GRU模型被确定为最佳性能技术。对 2018 年 5 月至 2020 年 12 月期间的估计表明，该事件似乎继续增加并保持在较高水平。
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.