Non-contact screening system based for COVID-19 on XGBoost and logistic regression

The coronavirus disease (COVID-19) effected a global health crisis in 2019, 2020, and beyond. Currently, methods such as temperature detection, clinical manifestations, and nucleic acid testing are used to comprehensively determine whether patients are infected with the severe acute respiratory synd...

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Veröffentlicht in:Computers in biology and medicine 2022-02, Vol.141, p.105003-105003, Article 105003
Hauptverfasser: Dong, Chunjiao, Qiao, Yixian, Shang, Chunheng, Liao, Xiwen, Yuan, Xiaoning, Cheng, Qin, Li, Yuxuan, Zhang, Jianan, Wang, Yunfeng, Chen, Yahong, Ge, Qinggang, Bao, Yurong
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container_title Computers in biology and medicine
container_volume 141
creator Dong, Chunjiao
Qiao, Yixian
Shang, Chunheng
Liao, Xiwen
Yuan, Xiaoning
Cheng, Qin
Li, Yuxuan
Zhang, Jianan
Wang, Yunfeng
Chen, Yahong
Ge, Qinggang
Bao, Yurong
description The coronavirus disease (COVID-19) effected a global health crisis in 2019, 2020, and beyond. Currently, methods such as temperature detection, clinical manifestations, and nucleic acid testing are used to comprehensively determine whether patients are infected with the severe acute respiratory syndrome coronavirus 2. However, during the peak period of COVID-19 outbreaks and in underdeveloped regions, medical staff and high-tech detection equipment were limited, resulting in the continued spread of the disease. Thus, a more portable, cost-effective, and automated auxiliary screening method is necessary. We aim to apply a machine learning algorithm and non-contact monitoring system to automatically screen potential COVID-19 patients. We used impulse-radio ultra-wideband radar to detect respiration, heart rate, body movement, sleep quality, and various other physiological indicators. We collected 140 radar monitoring data from 23 COVID-19 patients in Wuhan Tongji Hospital and compared them with 144 radar monitoring data from healthy controls. Then, the XGBoost and logistic regression (XGBoost + LR) algorithms were used to classify the data according to patients and healthy subjects. The XGBoost + LR algorithm demonstrated excellent discrimination (precision = 92.5%, recall rate = 96.8%, AUC = 98.0%), outperforming other single machine learning algorithms. Furthermore, the SHAP value indicates that the number of apneas during REM, mean heart rate, and some sleep parameters are important features for classification. The XGBoost + LR-based screening system can accurately predict COVID-19 patients and can be applied in hotels, nursing homes, wards, and other crowded locations to effectively help medical staff. •COVID-19 diagnosis is difficult in underdeveloped and understaffed areas.•Physiological indicators e.g., heart rate and sleep quality were measured.•XGBoost and logic regression were combined to classify patient data.•Achieved precision = 92.5%, recall rate = 96.8%, and AUC = 98.0%.•Apneas during REM, mean heart rate, and sleep parameters are key features.
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Currently, methods such as temperature detection, clinical manifestations, and nucleic acid testing are used to comprehensively determine whether patients are infected with the severe acute respiratory syndrome coronavirus 2. However, during the peak period of COVID-19 outbreaks and in underdeveloped regions, medical staff and high-tech detection equipment were limited, resulting in the continued spread of the disease. Thus, a more portable, cost-effective, and automated auxiliary screening method is necessary. We aim to apply a machine learning algorithm and non-contact monitoring system to automatically screen potential COVID-19 patients. We used impulse-radio ultra-wideband radar to detect respiration, heart rate, body movement, sleep quality, and various other physiological indicators. We collected 140 radar monitoring data from 23 COVID-19 patients in Wuhan Tongji Hospital and compared them with 144 radar monitoring data from healthy controls. Then, the XGBoost and logistic regression (XGBoost + LR) algorithms were used to classify the data according to patients and healthy subjects. The XGBoost + LR algorithm demonstrated excellent discrimination (precision = 92.5%, recall rate = 96.8%, AUC = 98.0%), outperforming other single machine learning algorithms. Furthermore, the SHAP value indicates that the number of apneas during REM, mean heart rate, and some sleep parameters are important features for classification. 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The XGBoost + LR-based screening system can accurately predict COVID-19 patients and can be applied in hotels, nursing homes, wards, and other crowded locations to effectively help medical staff. •COVID-19 diagnosis is difficult in underdeveloped and understaffed areas.•Physiological indicators e.g., heart rate and sleep quality were measured.•XGBoost and logic regression were combined to classify patient data.•Achieved precision = 92.5%, recall rate = 96.8%, and AUC = 98.0%.•Apneas during REM, mean heart rate, and sleep parameters are key features.</description><subject>Algorithms</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Global health</subject><subject>Heart rate</subject><subject>Hotels</subject><subject>Humans</subject><subject>Infectious diseases</subject><subject>Learning algorithms</subject><subject>Logistic Models</subject><subject>Logistic regression</subject><subject>Machine learning</subject><subject>Medical personnel</subject><subject>Medical research</subject><subject>Monitoring</subject><subject>Monitoring, Physiologic</subject><subject>Non-contact vital signs</subject><subject>Nucleic acids</subject><subject>Nursing homes</subject><subject>Patients</subject><subject>Public health</subject><subject>Radar</subject><subject>Radar data</subject><subject>Respiration</subject><subject>Respiratory diseases</subject><subject>SARS-CoV-2</subject><subject>Screening system</subject><subject>Severe acute respiratory syndrome</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>Sleep</subject><subject>Telemedicine</subject><subject>Ultrawideband radar</subject><subject>Viral diseases</subject><subject>XGBoost</subject><issn>0010-4825</issn><issn>1879-0534</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqFkU1vEzEQhi0EomnhLyBLXLhsGH_t2hckGqCtVFEJAeJmeb2zwVHWDvamUv89jlLKx4WTpfEz78y8LyGUwZIBa19vlj5Nuz6kCYclB85qWQGIR2TBdGcaUEI-JgsABo3UXJ2Q01I2ACBBwFNyImSnOWOwIJ8-ptj4FGfnZ1p8Rowhrmm5KzNOtHcFBzqmTFc3X6_eNczQFOm3i_OUykxdHOg2rUOZg6cZ1xlLCSk-I09Gty34_P49I18-vP-8umyuby6uVm-vG686MTdccolKqdG3qnfCtYitHltwynCj5ChHrzulOgNmgEGLzhjl3Kh4r6U0LYgz8uaou9v31QePcc5ua3c5TC7f2eSC_fsnhu92nW6tVq1Q_CDw6l4gpx97LLOdQvG43bqIaV8sV0ZDp6u9FX35D7pJ-xzreZa3XFTQtF2l9JHyOZWScXxYhoE9BGc39ndw9hCcPQZXW1_8ecxD46-kKnB-BLBaehsw2-IDRo9DyOhnO6Tw_yk_AWM3rXA</recordid><startdate>20220201</startdate><enddate>20220201</enddate><creator>Dong, Chunjiao</creator><creator>Qiao, Yixian</creator><creator>Shang, Chunheng</creator><creator>Liao, Xiwen</creator><creator>Yuan, Xiaoning</creator><creator>Cheng, Qin</creator><creator>Li, Yuxuan</creator><creator>Zhang, Jianan</creator><creator>Wang, Yunfeng</creator><creator>Chen, Yahong</creator><creator>Ge, Qinggang</creator><creator>Bao, Yurong</creator><general>Elsevier Ltd</general><general>Elsevier Limited</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M7P</scope><scope>M7Z</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-9141-4783</orcidid><orcidid>https://orcid.org/0000-0001-6892-3774</orcidid></search><sort><creationdate>20220201</creationdate><title>Non-contact screening system based for COVID-19 on XGBoost and logistic regression</title><author>Dong, Chunjiao ; 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Currently, methods such as temperature detection, clinical manifestations, and nucleic acid testing are used to comprehensively determine whether patients are infected with the severe acute respiratory syndrome coronavirus 2. However, during the peak period of COVID-19 outbreaks and in underdeveloped regions, medical staff and high-tech detection equipment were limited, resulting in the continued spread of the disease. Thus, a more portable, cost-effective, and automated auxiliary screening method is necessary. We aim to apply a machine learning algorithm and non-contact monitoring system to automatically screen potential COVID-19 patients. We used impulse-radio ultra-wideband radar to detect respiration, heart rate, body movement, sleep quality, and various other physiological indicators. We collected 140 radar monitoring data from 23 COVID-19 patients in Wuhan Tongji Hospital and compared them with 144 radar monitoring data from healthy controls. Then, the XGBoost and logistic regression (XGBoost + LR) algorithms were used to classify the data according to patients and healthy subjects. The XGBoost + LR algorithm demonstrated excellent discrimination (precision = 92.5%, recall rate = 96.8%, AUC = 98.0%), outperforming other single machine learning algorithms. Furthermore, the SHAP value indicates that the number of apneas during REM, mean heart rate, and some sleep parameters are important features for classification. The XGBoost + LR-based screening system can accurately predict COVID-19 patients and can be applied in hotels, nursing homes, wards, and other crowded locations to effectively help medical staff. •COVID-19 diagnosis is difficult in underdeveloped and understaffed areas.•Physiological indicators e.g., heart rate and sleep quality were measured.•XGBoost and logic regression were combined to classify patient data.•Achieved precision = 92.5%, recall rate = 96.8%, and AUC = 98.0%.•Apneas during REM, mean heart rate, and sleep parameters are key features.</abstract><cop>United States</cop><pub>Elsevier Ltd</pub><pmid>34782110</pmid><doi>10.1016/j.compbiomed.2021.105003</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0001-9141-4783</orcidid><orcidid>https://orcid.org/0000-0001-6892-3774</orcidid><oa>free_for_read</oa></addata></record>
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subjects Algorithms
Coronaviruses
COVID-19
Global health
Heart rate
Hotels
Humans
Infectious diseases
Learning algorithms
Logistic Models
Logistic regression
Machine learning
Medical personnel
Medical research
Monitoring
Monitoring, Physiologic
Non-contact vital signs
Nucleic acids
Nursing homes
Patients
Public health
Radar
Radar data
Respiration
Respiratory diseases
SARS-CoV-2
Screening system
Severe acute respiratory syndrome
Severe acute respiratory syndrome coronavirus 2
Sleep
Telemedicine
Ultrawideband radar
Viral diseases
XGBoost
title Non-contact screening system based for COVID-19 on XGBoost and logistic regression
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