LSTM algorithm optimization for COVID-19 prediction model
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...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-02-01
|
| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024021893 |
| _version_ | 1797267689886973952 |
|---|---|
| author | Irwan Sembiring Sri Ngudi Wahyuni Eko Sediyono |
| author_facet | Irwan Sembiring Sri Ngudi Wahyuni Eko Sediyono |
| author_sort | Irwan Sembiring |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T19:41:47Z |
| format | Article |
| id | doaj.art-a3c088079f9142219e4e4792b82c381b |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-25T01:20:35Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-a3c088079f9142219e4e4792b82c381b2024-03-09T09:27:24ZengElsevierHeliyon2405-84402024-02-01104e26158LSTM algorithm optimization for COVID-19 prediction modelIrwan Sembiring0Sri Ngudi Wahyuni1Eko Sediyono2Satya Wacana Christian University, 50711, Salatiga, IndonesiaUniversitas Amikom Yogyakarta, 55581, Indonesia; Corresponding author.Satya Wacana Christian University, 50711, Salatiga, IndonesiaThe 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.http://www.sciencedirect.com/science/article/pii/S2405844024021893COVID-19Time series predictionLSTM modelOptimization |
| spellingShingle | Irwan Sembiring Sri Ngudi Wahyuni Eko Sediyono LSTM algorithm optimization for COVID-19 prediction model Heliyon COVID-19 Time series prediction LSTM model Optimization |
| title | LSTM algorithm optimization for COVID-19 prediction model |
| title_full | LSTM algorithm optimization for COVID-19 prediction model |
| title_fullStr | LSTM algorithm optimization for COVID-19 prediction model |
| title_full_unstemmed | LSTM algorithm optimization for COVID-19 prediction model |
| title_short | LSTM algorithm optimization for COVID-19 prediction model |
| title_sort | lstm algorithm optimization for covid 19 prediction model |
| topic | COVID-19 Time series prediction LSTM model Optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024021893 |
| work_keys_str_mv | AT irwansembiring lstmalgorithmoptimizationforcovid19predictionmodel AT sringudiwahyuni lstmalgorithmoptimizationforcovid19predictionmodel AT ekosediyono lstmalgorithmoptimizationforcovid19predictionmodel |