Real-time internet of medical things framework for early detection of Covid-19

Abstract

The Covid-19 pandemic is a deadly epidemic and continues to affect all world. This situation dragged the countries into a global crisis and caused the collapse of some health systems. Therefore, many technologies are needed to slow down the spread of the Covid-19 epidemic and produce solutions. In this context, some developments have been made with artificial intelligence, machine learning and deep learning support systems in order to alleviate the burden on the health system. In this study, a new Internet of Medical Things (IoMT) framework is proposed for the detection and early prevention of Covid-19 infection. In the proposed IoMT framework, a Covid-19 scenario consisting of various numbers of sensors is created in the Riverbed Modeler simulation software. The health data produced in this scenario are analyzed in real time with Apache Spark technology, and disease prediction is made. In order to provide more accurate results for Covid-19 disease prediction, Random Forest and Gradient Boosted Tree (GBT) Ensemble Learning classifiers, which are formed by Decision Tree classifiers, are compared for the performance evaluation. In addition, throughput, end-to-end delay results and Apache Spark data processing performance of heterogeneous nodes with different priorities are analyzed in the Covid-19 scenario. The MongoDB NoSQL database is used in the IoMT framework to store big health data produced in real time and use it in subsequent processes. The proposed IoMT framework experimental results show that the GBTs classifier has the best performance with 95.70% training, 95.30% test accuracy and 0.970 area under the curve (AUC) values. Moreover, the promising real-time performances of wireless body area network (WBAN) simulation scenario and Apache Spark show that they can be used for the early detection of Covid-19 disease.

Prognosis of COVID‐19 patients using lab tests: A data mining approach

“…Many studies have been done in medical sciences to predict the course of diseases using data mining models. A study by Che and others in 2016 42 on the pediatric ICU data set for acute lung injury (ALI), using a method called interpretable mimic learning, which used the GBT model, showed that GBT could recognize important markers of mortality and ventilator‐free days prediction tasks. The 2020 study by Pan et al was conducted on a database of patients with COVID‐19 in the ICU to compare conventional logistic regression methods and four ML algorithms.…”

“…It showed that the multilayer perceptron (MLP) model performed better than the GBT model. 2 , 42 , 43 …”

“…Many studies have been done in medical sciences to predict the course of diseases using data mining models. A study by Che and others in 2016 42 on the pediatric ICU data set for acute lung injury (ALI), using a method called interpretable mimic learning, which used the GBT model, showed that GBT could recognize important markers of The GBT model is even used for early prediction of COVID- showed that the multilayer perceptron (MLP) model performed better than the GBT model. 2,42,43 According to this study and previous studies, we believe that regarding COVID-19 disease, the Gradient Boosted Trees model seems to have the best performance for laboratory tests such as ALT to predict the mortality of COVID-19 patients.…”

“…In the study conducted by Akbulut et al on Genomic Biomarkers of Metagenomic Next‐Generation Sequencing Data. It showed that the multilayer perceptron (MLP) model performed better than the GBT model 2,42,43 …”

Enhanced Patient-Centricity: How the Biopharmaceutical Industry Is Optimizing Patient Care through AI/ML/DL

“…For example, one of these articles showed the relationship between natural language and viral evolution [ 36 ]. Additional pandemic-specific articles cover a wide range of topics from contact tracing, detection, diagnosis, to drug repurposing (e.g., [ 89 , 90 , 91 , 92 ]).…”