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Non-contact screening system based for COVID-19 on XGBoost and logistic regression.
Dong, Chunjiao; Qiao, Yixian; Shang, Chunheng; Liao, Xiwen; Yuan, Xiaoning; Cheng, Qin; Li, Yuxuan; Zhang, Jianan; Wang, Yunfeng; Chen, Yahong; Ge, Qinggang; Bao, Yurong.
  • Dong C; Institute of Microelectronics of Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
  • Qiao Y; Department of Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China.
  • Shang C; Institute of Microelectronics of Chinese Academy of Sciences, Beijing, China.
  • Liao X; Institute of Microelectronics of Chinese Academy of Sciences, Beijing, China.
  • Yuan X; Department of Nosocomial Infection Management, Peking University Third Hospital, Beijing, China.
  • Cheng Q; Department of Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China.
  • Li Y; Department of Critical Care Medicine, Peking University Third Hospital, Beijing, China.
  • Zhang J; Department of Critical Care Medicine, Peking University Third Hospital, Beijing, China.
  • Wang Y; Institute of Microelectronics of Chinese Academy of Sciences, Beijing, China. Electronic address: wangyunfeng@ime.ac.cn.
  • Chen Y; Department of Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China. Electronic address: chenyahong@vip.sina.com.
  • Ge Q; Department of Critical Care Medicine, Peking University Third Hospital, Beijing, China. Electronic address: qingganggelin@126.com.
  • Bao Y; Department of Medical Quality Management and Telemedicine, The Second Medical Center of PLA General Hospital, Beijing, China. Electronic address: baoyurong1@163.com.
Comput Biol Med ; 141: 105003, 2022 02.
Article in English | MEDLINE | ID: covidwho-1517110
ABSTRACT

BACKGROUND:

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.

OBJECTIVE:

We aim to apply a machine learning algorithm and non-contact monitoring system to automatically screen potential COVID-19 patients.

METHODS:

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.

RESULTS:

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.

CONCLUSION:

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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Diagnostic study / Prognostic study Limits: Humans Language: English Journal: Comput Biol Med Year: 2022 Document Type: Article Affiliation country: J.compbiomed.2021.105003

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Diagnostic study / Prognostic study Limits: Humans Language: English Journal: Comput Biol Med Year: 2022 Document Type: Article Affiliation country: J.compbiomed.2021.105003