Non-contact screening system based for COVID-19 on XGBoost and logistic regression
| Autor: | Yurong Bao, Yuxuan Li, Qin Cheng, Yahong Chen, Qinggang Ge, Chunheng Shang, Yixian Qiao, Chunjiao Dong, Yunfeng Wang, Xiwen Liao, Jianan Zhang, Xiaoning Yuan |
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| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Coronavirus disease 2019 (COVID-19) Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Logistic regression Health Informatics Disease Screening system Article law.invention law Heart rate medicine Humans Radar Monitoring Physiologic business.industry SARS-CoV-2 COVID-19 Body movement Monitoring system Computer Science Applications Logistic Models Emergency medicine Non-contact vital signs business XGBoost |
| Zdroj: | Computers in Biology and Medicine |
| ISSN: | 1879-0534 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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