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By using a connected network, a healthcare system with the Internet of Things (IoT) functionality can effectively monitor COVID-19 cases. IoT helps a COVID-19 patient recognize symptoms and receive better therapy more quickly. A critical component in measuring, evaluating, and diagnosing the risk of infection is artificial intelligence (AI). It can be used to anticipate cases and forecast the alternate incidences number, retrieved instances, and injuries. In the context of COVID-19, IoT technologies are employed in specific patient monitoring and diagnosing processes to reduce COVID-19 exposure to others.
- machine learning
- convolutional neural network (CNN)
- gated recurrent unit (GRU)
- Internet of Medical Things (IoMT)
- COVID-19 pandemic
- death prediction
1. Introduction
Presently, there are many diseases that have become prevalent [1]. The COVID-19 coronavirus illness was initially identified in December 2019 in China, in Wuhan, and has since spread around the world. The virus spreads quickly because it is easily transmitted from one individual to another [2]. Technology and science play a significant part in this confusing conflict. China focused on medical research and using robots to deliver food and medical supplies, automatons to clean up open spaces, and broadcasting and watching sound information exchange publicly to encourage people to remain at home. In order to help COVID-19 patients, a lot of human expertise was employed to detect the new particles in transit [3]. Numerous studies are being conducted to monitor, trace contacts, forecast, and diagnose the COVID-19 illness. One of these innovations is the Internet of Things (IoT), which is attracting international attention for its growing role in healthcare systems’ ability to forecast, identify, prevent, and monitor the majority of infectious illnesses. Similar to how it aids in the battle against COVID-19, it also helps in the detection of the COVID-19 epidemic through monitoring, contract tracking, and connecting with IoT-based efficient technologies [4]. IoT is a network of connected electronic devices, actuators, sensors, and data that are collected in their raw form and delivered online via the internet [5].
Healthcare represents one of the vital domains that employ IoT systems and smart devices for supervision. IoT is a successful field for many sectors and academic subjects. IoT transition supplies modern healthcare services with scientific and socioeconomic perspectives. Since the outbreak of the pandemic, several scientific groups have stepped up their efforts to employ a variety of methods to address this global issue. IoT techniques are utilized in certain procedures, such as prenatal screening, patient monitoring, and post-patient event response, to reduce COVID-19 exposure to other individuals [6]. The IoT-based healthcare system is described in depth by the Internet of Medical Things (IoMT) [7]. When employed during this epidemic, the IoMT can assist patients in receiving appropriate medical treatment at home, and healthcare officials and governments can utilize the extensive dataset built for COVID-19 spread control. People with mild symptoms can buy diagnostic and medical equipment, including thermometers, smart watches, smart helmets, drugs, protective masks, and tracking infection packages. Patients can routinely upload overall health records via a wireless network and the internet to medical cloud servers, and these data can be sent to the closest hospitals, health centers, or clinics, as well as the Centers for Disease Control (CDC) [8]. IoMT offers a platform for smart devices and sensors to communicate effectively in a smart environment, and makes it simple to interchange data and information online.
A critical component in measuring, evaluating, and diagnosing diseases is artificial intelligence (AI). It can be used to anticipate cases as well as forecast the number of alternate incidences, recovered cases, and injuries, along with specific software engineering analyzers that concentrate on the detection of patients through the production of medical images like CT filters and X beams; a lot of professionals employ AI to uncover novel drugs and treatments [3]. AI is made to act and think like a human brain, automating many tasks by imitating its thought processes. In preparation for COVID-19’s eventual cross-country accessibility, machine learning (ML) and deep learning (DL) techniques can be utilized to track typical behavior using open data sources from real-time applications. These techniques can forecast the immediate future and aid in minimizing the negative impacts of COVID-19 [9,10,11]. Around the world, concerns have been raised about the COVID-19 pandemic strategy’s capabilities and delivery, quick response, linked information, and evaluation [6]. Even though the current deep learning techniques have considerably improved their performance for COVID-19 detection, the bulk of these techniques still have overfitting issues [12]. Advanced healthcare informatics and computational intelligence are enabling the development of secure and patient-oriented IoT systems that use BiLSTM deep learning and decision tree models to support automated diagnosis [13].
Optimization is a powerful tool used in various domains, and it plays a significant role in the medical field. Optimization aims to achieve the best possible outcomes or decisions under specific conditions based on a set of variables and defined criteria. In the medical domain, optimization is applied in diverse areas, such as the prediction and classification of monkeypox disease [14,15,16], feature selection and classification in diagnostic breast cancer [17], classification of diabetes [18], neurodegenerative disorders [19], and classification of COVID-19 in chest X-ray images [20]. The use of optimization in the medical field contributes to enhancing patient outcomes and maximizing the utilization of available resources.
An IoT-based system is necessary to address the monitoring and diagnostic issues, as it will aid in implementing stay-at-home protocols and decreasing the number of medical resources required [19]. With this method, information on healthcare facilities can be gathered, allowing for more efficient medical care to be established and more equitable distribution of government and private donations of medical supplies and equipment to hospitals and clinics [20]. In order to provide timely and effective medical services, especially in light of COVID-19, the disciplines of IoT and AI have been forcefully urged to routinely automate and simplify numerous duties for health professionals. Researchers delve into the role that IoMT and AI will play in bringing healthcare to a completely new level in the face of the COVID-19 epidemic. The hybrid deep learning model of a convolutional neural network with a gated recurrent unit for predicting COVID-19 mortality via the IoT can be combined with a biosensor for real-time patient monitoring. A biosensor is a device that detects and measures biological, chemical, or physical signals in the body. By integrating the biosensor with the hybrid CNN-GRU model, the system can collect continuous and accurate data to improve the accuracy of COVID-19 mortality prediction. This can lead to better patient outcomes and more efficient resource allocation in clinical settings.
2. Use of Biosensors and Internet of Medical Things (IoMT) in COVID-19 Prediction
The use of biosensors and the Internet of Medical Things (IoMT) in COVID-19 prediction is an active area of research. Biosensors can detect biological signals and transmit data in real-time to IoMT-enabled devices, allowing for the continuous monitoring of patients [21]. These data can be used in combination with machine learning models to predict disease progression, severity, and mortality. Several studies have investigated the use of biosensors and IoMT in COVID-19 prediction. For example, a recent study developed a biosensor-based system that uses artificial intelligence algorithms to predict COVID-19 severity and mortality. The system integrates biosensors with IoMT-enabled devices, allowing real-time patient monitoring and data collection [22].
Another study used a wearable biosensor to monitor COVID-19 patients and predict disease severity based on changes in heart rate variability [23]. The study found that changes in heart rate variability were associated with disease severity and could be used to predict disease progression. Overall, the integration of biosensors and IoMT has the potential to improve COVID-19 prediction, leading to better patient outcomes and a more efficient allocation of healthcare resources [24]. Healthcare is being revolutionized by cutting-edge innovations like IoT and smart sensors, robots, artificial intelligence (AI), blockchain, machine learning (ML), augmented reality (AR), virtual reality (VR), big data, cloud computing, drones and intelligent mobile applications, 5G, and so on. Pre-screening, early identification, monitoring quarantined/infected persons, estimating future infection rates, and other methods of dealing with COVID-19 were discussed. Research opportunities made possible by the deployment of cutting-edge technology to combat the COVID-19 pandemic are also explored [25,26,27]. A developed neural network model presented by Wieczorek et al. [28] that showed the spread of the COVID-19 virus using the NAdam optimizer achieved 99.00% accuracy.
A six-tiered architecture of IoT tools for controlling the deadly COVID-19 virus was presented by Farhana Ajaz et al. [29]. The function of machine learning strategies in the identification of COVID-19 was explored. The effects of COVID-19 were mitigated in a number of ways, some of which made use of IoT technology. In addition, IoT could be applied in the medical field to guarantee people’s safety and health while keeping expenses down. Mir et al. [30] presented a real-time IoT-enabled architecture for COVID-19 diagnosis and prediction by gathering symptomatic indicators and better evaluating the virus’s characteristics. By mining health information acquired in real-time detection from sensing devices and IoT objects, the framework was able to determine the existence of the COVID-19 virus. The framework’s four primary parts were the data collection hub, the data analytics hub, the diagnostics hub, and the cloud system. This paper offered five machine learning methods for real-time pointing and detection of COVID-19 suspects. Results indicated an accuracy of 95% or higher using the applied machine learning methods.
Anita S. Kini et al. [31] developed a system for the screening of possible instances of COVID-19 using an ensemble of DL models and the IoT. The ensemble was made up of three common pre-trained DL models. The CT scans were collected using clinical IoT devices, and the automated diagnoses were processed by IoT platforms. Over the course of a four-class dataset, the proposed methodology was evaluated against 13 competing models. From their experiments, the suggested ensembled DL technique achieved a 98.98% success rate. As a result, the suggested methodology accelerated the process of identifying COVID-19. Fatema Al-Dhaen et al. [32] developed a simulation to study how ethical AI could help the advancement of IoMT in medical settings. Asghari et al. [33] suggested an IoT-based prediction method for colorectal cancer (CRC). Through the use of wearable embedded devices and healthcare IoT devices, it generates a CRC prediction technique by collecting vital clinical data via IoMT sensors and devices, enabling the medical staff to track the biomarkers of an aging individual over time.
To easily recognize the COVID-19 CT images and chest X-rays available to the public, a hybrid framework of the artificial neural network with parameters optimized using the butterfly optimization algorithm has been suggested and compared to the pre-trained GoogLeNet, AlexNet, and the SVM for COVID-19 recognition. With average accuracy of 90.48, 86.76, 84.97%, and 81.09 for the proposed model, AlexNet, GoogLeNet, and SVM, the experimental findings validated the effectiveness of the suggested model [34]. Khan et al. [35] suggested two novel DL frameworks, Deep Boosted Hybrid Learning (DBHL) and Deep Hybrid Learning (DHL) for efficient COVID-19 identification in the X-ray database. On the radiologist-verified chest X-ray database, the suggested COVID-19 identification frameworks were compared against traditional CNNs. Experiments showed that the DBHL, which combined the feature spaces of two deep CNNs, achieved high levels of accuracy equal to 98.53%.
Shawni Dutta et al. [36] suggested a technique for checking verification using the principles of DL neural networks. The framework integrated long short-term memory (LSTM) and gated recurrent unit (GRU) for training the database, and the outcomes of the predictions matched those made by clinical physicians. The predictions were checked against the source data using some metric that had been established. The experimental outcomes demonstrated the efficacy of the suggested method in producing appropriate outcomes in light of the serious illness epidemic. Soudeh Ghafouri et al. [37] integrated LSTM, recurrent neural network, multilayer perceptron, and adaptive neuro-fuzzy inference system. Researchers evaluated multiple machine learning strategies for their ability to foretell the spreading of COVID-19. These models integrated data from illnesses with comparable patterns to COVID-19, allowing for the discovery of learning indicators that affect differences in COVID-19 dissemination across different locations or populations, as well as the implementation of what-if scenarios based on those approaches. Thus, these techniques, if used in policymaking, would aid in the development of effective interventions and the avoidance of ineffective restraints.
This entry is adapted from the peer-reviewed paper 10.3390/biomimetics8070552
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