Sembiring Irwan, Wahyuni Sri Ngudi, Sediyono Eko
Satya Wacana Christian University, 50711, Salatiga, Indonesia.
Universitas Amikom Yogyakarta, 55581, Indonesia.
Heliyon. 2024 Feb 16;10(4):e26158. doi: 10.1016/j.heliyon.2024.e26158. eCollection 2024 Feb 29.
The development of predictive models for infectious diseases, specifically COVID-19, is an important step in early control efforts to reduce the mortality rate. However, traditional time series prediction models used to analyze the disease spread trends often encounter challenges related to accuracy, necessitating the need to develop prediction models with enhanced accuracy. Therefore, this research aimed to develop a prediction model based on the Long Short-Term Memory (LSTM) networks to better predict the number of confirmed COVID-19 cases. The proposed optimized LSTM (popLSTM) model was compared with Basic LSTM and improved MinMaxScaler developed earlier using COVID-19 dataset taken from previous research. The dataset was collected from four countries with a high daily increase in confirmed cases, including Hong Kong, South Korea, Italy, and Indonesia. The results showed significantly improved accuracy in the optimized model compared to the previous research methods. The contributions of popLSTM included 1) Incorporating the output results on the output gate to effectively filter more detailed information compared to the previous model, and 2) Reducing the error value by considering the hidden state on the output gate to improve accuracy. popLSTM in this experiment exhibited a significant 4% increase in accuracy.
开发传染病预测模型，尤其是针对新型冠状病毒肺炎（COVID-19）的预测模型，是降低死亡率早期防控工作的重要一步。然而，用于分析疾病传播趋势的传统时间序列预测模型在准确性方面常常面临挑战，因此需要开发精度更高的预测模型。所以，本研究旨在基于长短期记忆（LSTM）网络开发一个预测模型，以更好地预测新型冠状病毒肺炎确诊病例数。将所提出的优化长短期记忆（popLSTM）模型与基本长短期记忆模型以及早期使用从先前研究中获取的新型冠状病毒肺炎数据集开发的改进型最小-最大归一化（MinMaxScaler）进行了比较。该数据集来自确诊病例每日增幅较高的四个国家，包括中国香港、韩国、意大利和印度尼西亚。结果表明，与先前的研究方法相比，优化后的模型在准确性上有显著提高。popLSTM的贡献包括：1）与先前模型相比，将输出结果纳入输出门，以有效过滤更详细的信息；2）通过考虑输出门上的隐藏状态来降低误差值，从而提高准确性。本实验中的popLSTM在准确性上显著提高了4%。
