COVID-19 has depleted healthcare systems around the world. Extreme conditions must be defined as soon as possible so that services and treatment can be deployed and intensified. Many biomarkers are being investigated in order to track the patient's condition. Unfortunately, this may interfere with the symptoms of other diseases, making it more difficult for a specialist to diagnose or predict the severity level of the case. This research develops a Smart Healthcare System for Severity Prediction and Critical Tasks Management (SHSSP-CTM) for COVID-19 patients. On the one hand, a machine learning (ML) model is projected to predict the severity of COVID-19 disease. On the other hand, a multi-agent system is proposed to prioritize patients according to the seriousness of the COVID-19 condition and then provide complete network management from the edge to the cloud. Clinical data, including Internet of Medical Things (IoMT) sensors and Electronic Health Record (EHR) data of 78 patients from one hospital in the Wasit Governorate, Iraq, were used in this study. Different data sources are fused to generate new feature pattern. Also, data mining techniques such as normalization and feature selection are applied. Two models, specifically logistic regression (LR) and random forest (RF), are used as baseline severity predictive models. A multi-agent algorithm (MAA), consisting of a personal agent (PA) and fog node agent (FNA), is used to control the prioritization process of COVID-19 patients. The highest prediction result is achieved based on data fusion and selected features, where all examined classifiers observe a significant increase in accuracy. Furthermore, compared with state-of-the-art methods, the RF model showed a high and balanced prediction performance with 86% accuracy, 85.7% F-score, 87.2% precision, and 86% recall. In addition, as compared to the cloud, the MAA showed very significant performance where the resource usage was 66% in the proposed model and 34% in the traditional cloud, the delay was 19% in the proposed model and 81% in the cloud, and the consumed energy was 31% in proposed model and 69% in the cloud. The findings of this study will allow for the early detection of three severity cases, lowering mortality rates.

College of Agriculture Al Muthanna University Samawah 66001 Iraq

College of Computer Science and Information Technology University of Anbar 11 Ramadi Anbar Iraq

College of Engineering University of Warith Al Anbiyaa Karbala Iraq

Department of Biochemistry College of Medicine University of Wasit Wasit Iraq

Department of Computer Science and Artificial Intelligence Wenzhou University Wenzhou 325035 China

Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and Communication University of Žilina Žilina Slovakia

Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB Technical University of Ostrava Ostrava Poruba Czech Republic

Engineering Department University of Technology Iraq Baghdad Iraq

Ministry of Education General Directorate of Curricula Pure Science Department Baghdad Iraq

Noroff University College Kristiansand Norway

School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad 44500 Pakistan

Comput Intell Neurosci. 2023 Oct 4;2023:9820684. doi: 10.1155/2023/9820684. PubMed

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