Abstract
The COronaVIrus Disease 2019 (COVID-19) pandemic is unfortunately highly transmissible across the people. In order to detect and track the suspected COVID-19 infected people and consequently limit the pandemic spread, this paper entails a framework integrating the machine learning (ML), cloud, fog, and Internet of Things (IoT) technologies to propose a novel smart COVID-19 disease monitoring and prognosis system. The proposal leverages the IoT devices that collect streaming data from both medical (e.g., X-ray machine, lung ultrasound machine, etc.) and non-medical (e.g., bracelet, smartwatch, etc.) devices. Moreover, the proposed hybrid fog-cloud framework provides two kinds of federated ML as a service (federated MLaaS); (i) the distributed batch MLaaS that is implemented on the cloud environment for a long-term decision-making, and (ii) the distributed stream MLaaS, which is installed into a hybrid fog-cloud environment for a short-term decision-making. The stream MLaaS uses a shared federated prediction model stored into the cloud, whereas the real-time symptom data processing and COVID-19 prediction are done into the fog. The federated ML models are determined after evaluating a set of both batch and stream ML algorithms from the Python’s libraries. The evaluation considers both the quantitative (i.e., performance in terms of accuracy, precision, root mean squared error, and F1 score) and qualitative (i.e., quality of service in terms of server latency, response time, and network latency) metrics to assess these algorithms. This evaluation shows that the stream ML algorithms have the potential to be integrated into the COVID-19 prognosis allowing the early predictions of the suspected COVID-19 cases.
Highlights
Healthcare is one of the important application fields that require real-time and accurate results
We provide (i) a batch MLs as a Service (MLaaS) implemented on the cloud environment for the long-term decision making, (ii) a stream MLaaS installed on the fog environment for the short-term decision making, and (iii) the both machine learning (ML) types simultaneously based on their dependencies and their connections
Data are divided into three sets of sequences [26], such as (i) training set for training the ML model by using one or more algorithm(s), (ii) validation set for predicting the ML model and finding the best one, and (iii) testing set for predicting final model performance
Summary
Healthcare is one of the important application fields that require real-time and accurate results. Some other studies [18,19,20] presented ML-based framework for predicting whether a suspected person is COVID-19 infected or not Their frameworks adopted a set of IoT devices such as temperature, heartbeat, oxygen saturation monitor, etc., which might collect health data and help to monitor the users in real time. The evaluation of the proposed framework demonstrates its performance in terms of accuracy, response time, precision, etc., for COVID-19 monitoring and prognosis. The experiment results demonstrate that the most accurate predictions are those obtained while adopting no-shuffle train/test split strategy and MLP Classifier This quantitatively outperforms other strategies and algorithms by providing the best values in terms of performance metrics such as accuracy, precision, root-mean-squared error (RMSE), and F1 score. The implementation of our framework and its evaluation are given
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