LSTM algorithm optimization for COVID-19 prediction model
Irwan Sembiring, Sri Wahyuni, Eko Sediyono
Abstract
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.