Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application

• URL: http://arxiv.org/abs/2209.03522v1
• Date: Thu, 8 Sep 2022 01:35:45 GMT
• Title: Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application
• Authors: Andrei Velichko, Mehmet Tahir Huyut, Maksim Belyaev, Yuriy Izotov and Dmitry Korzun
• Abstract summary: Histogram-based Gradient Boosting (HGB) was used to detect the disease with 100% accuracy, learning time 6.39 sec. We propose to use these 11 traits and their combinations as important biomarkers for ML sensors in diagnosis of the disease, supporting edge computing on Arduino and cloud IoT service.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Healthcare digitalization needs effective methods of human sensorics, when various parameters of the human body are instantly monitored in everyday life and connected to the Internet of Things (IoT). In particular, Machine Learning (ML) sensors for the prompt diagnosis of COVID-19 is an important case for IoT application in healthcare and Ambient Assistance Living (AAL). Determining the infected status of COVID-19 with various diagnostic tests and imaging results is costly and time-consuming. The aim of this study is to provide a fast, reliable and economical alternative tool for the diagnosis of COVID-19 based on the Routine Blood Values (RBV) values measured at admission. The dataset of the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 test results and 51 routine blood values. In this study, 13 popular classifier machine learning models and LogNNet neural network model were exanimated. The most successful classifier model in terms of time and accuracy in the detection of the disease was the Histogram-based Gradient Boosting (HGB). The HGB classifier identified the 11 most important features (LDL, Cholesterol, HDL-C, MCHC, Triglyceride, Amylase, UA, LDH, CK-MB, ALP and MCH) to detect the disease with 100% accuracy, learning time 6.39 sec. In addition, the importance of single, double and triple combinations of these features in the diagnosis of the disease was discussed. We propose to use these 11 traits and their combinations as important biomarkers for ML sensors in diagnosis of the disease, supporting edge computing on Arduino and cloud IoT service.

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