Artificial intelligence in healthcare: combining deep learning and Bayesian optimization to forecast COVID-19 confirmed cases
Healthcare is a topic of significant concern within the academic and business sectors. The COVID-19 pandemic has had a considerable effect on the health of people worldwide. The rapid increase in cases adversely affects a nation's economy, public health, and residents' social and personal...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-01-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2023.1327355/full |
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| author | Areej Alhhazmi Ahmad Alferidi Yahya A. Almutawif Hatim Makhdoom Hibah M. Albasri Ben Slama Sami |
| author_facet | Areej Alhhazmi Ahmad Alferidi Yahya A. Almutawif Hatim Makhdoom Hibah M. Albasri Ben Slama Sami |
| author_sort | Areej Alhhazmi |
| collection | DOAJ |
| description | Healthcare is a topic of significant concern within the academic and business sectors. The COVID-19 pandemic has had a considerable effect on the health of people worldwide. The rapid increase in cases adversely affects a nation's economy, public health, and residents' social and personal well-being. Improving the precision of COVID-19 infection forecasts can aid in making informed decisions regarding interventions, given the pandemic's harmful impact on numerous aspects of human life, such as health and the economy. This study aims to predict the number of confirmed COVID-19 cases in Saudi Arabia using Bayesian optimization (BOA) and deep learning (DL) methods. Two methods were assessed for their efficacy in predicting the occurrence of positive cases of COVID-19. The research employed data from confirmed COVID-19 cases in Saudi Arabia (SA), the United Kingdom (UK), and Tunisia (TU) from 2020 to 2021. The findings from the BOA model indicate that accurately predicting the number of COVID-19 positive cases is difficult due to the BOA projections needing to align with the assumptions. Thus, a DL approach was utilized to enhance the precision of COVID-19 positive case prediction in South Africa. The DQN model performed better than the BOA model when assessing RMSE and MAPE values. The model operates on a local server infrastructure, where the trained policy is transmitted solely to DQN. DQN formulated a reward function to amplify the efficiency of the DQN algorithm. By examining the rate of change and duration of sleep in the test data, this function can enhance the DQN model's training. Based on simulation findings, it can decrease the DQN work cycle by roughly 28% and diminish data overhead by more than 50% on average. |
| first_indexed | 2024-03-08T05:44:01Z |
| format | Article |
| id | doaj.art-9aef3ac633f4477499e11a48a3b25426 |
| institution | Directory Open Access Journal |
| issn | 2624-8212 |
| language | English |
| last_indexed | 2024-03-08T05:44:01Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj.art-9aef3ac633f4477499e11a48a3b254262024-02-05T14:52:19ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122024-01-01610.3389/frai.2023.13273551327355Artificial intelligence in healthcare: combining deep learning and Bayesian optimization to forecast COVID-19 confirmed casesAreej Alhhazmi0Ahmad Alferidi1Yahya A. Almutawif2Hatim Makhdoom3Hibah M. Albasri4Ben Slama Sami5Medical Laboratories Technology Department, College of Applied Medical Sciences, Taibah University, Al-Madinah Al-Munawarah, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, Taibah University, Al-Madinah Al-Munawarah, Saudi ArabiaMedical Laboratories Technology Department, College of Applied Medical Sciences, Taibah University, Al-Madinah Al-Munawarah, Saudi ArabiaMedical Laboratories Technology Department, College of Applied Medical Sciences, Taibah University, Al-Madinah Al-Munawarah, Saudi ArabiaDepartment of Biology, College of Science, Taibah University, Al-Madinah Al-Munawarah, Saudi ArabiaComputer Sciences Department, The Applied College, King Abdulaziz, Saudi Arabia University, Jeddah, Saudi ArabiaHealthcare is a topic of significant concern within the academic and business sectors. The COVID-19 pandemic has had a considerable effect on the health of people worldwide. The rapid increase in cases adversely affects a nation's economy, public health, and residents' social and personal well-being. Improving the precision of COVID-19 infection forecasts can aid in making informed decisions regarding interventions, given the pandemic's harmful impact on numerous aspects of human life, such as health and the economy. This study aims to predict the number of confirmed COVID-19 cases in Saudi Arabia using Bayesian optimization (BOA) and deep learning (DL) methods. Two methods were assessed for their efficacy in predicting the occurrence of positive cases of COVID-19. The research employed data from confirmed COVID-19 cases in Saudi Arabia (SA), the United Kingdom (UK), and Tunisia (TU) from 2020 to 2021. The findings from the BOA model indicate that accurately predicting the number of COVID-19 positive cases is difficult due to the BOA projections needing to align with the assumptions. Thus, a DL approach was utilized to enhance the precision of COVID-19 positive case prediction in South Africa. The DQN model performed better than the BOA model when assessing RMSE and MAPE values. The model operates on a local server infrastructure, where the trained policy is transmitted solely to DQN. DQN formulated a reward function to amplify the efficiency of the DQN algorithm. By examining the rate of change and duration of sleep in the test data, this function can enhance the DQN model's training. Based on simulation findings, it can decrease the DQN work cycle by roughly 28% and diminish data overhead by more than 50% on average.https://www.frontiersin.org/articles/10.3389/frai.2023.1327355/fullartificial intelligencealgorithmBayesian optimizationCOVID-19deep reinforcement learningdecision-making |
| spellingShingle | Areej Alhhazmi Ahmad Alferidi Yahya A. Almutawif Hatim Makhdoom Hibah M. Albasri Ben Slama Sami Artificial intelligence in healthcare: combining deep learning and Bayesian optimization to forecast COVID-19 confirmed cases Frontiers in Artificial Intelligence artificial intelligence algorithm Bayesian optimization COVID-19 deep reinforcement learning decision-making |
| title | Artificial intelligence in healthcare: combining deep learning and Bayesian optimization to forecast COVID-19 confirmed cases |
| title_full | Artificial intelligence in healthcare: combining deep learning and Bayesian optimization to forecast COVID-19 confirmed cases |
| title_fullStr | Artificial intelligence in healthcare: combining deep learning and Bayesian optimization to forecast COVID-19 confirmed cases |
| title_full_unstemmed | Artificial intelligence in healthcare: combining deep learning and Bayesian optimization to forecast COVID-19 confirmed cases |
| title_short | Artificial intelligence in healthcare: combining deep learning and Bayesian optimization to forecast COVID-19 confirmed cases |
| title_sort | artificial intelligence in healthcare combining deep learning and bayesian optimization to forecast covid 19 confirmed cases |
| topic | artificial intelligence algorithm Bayesian optimization COVID-19 deep reinforcement learning decision-making |
| url | https://www.frontiersin.org/articles/10.3389/frai.2023.1327355/full |
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