TY - UNPB
T1 - Real-Time Magnetic Tracking and Diagnosis of COVID-19 via Machine Learning
AU - Nguyen, Dang
AU - Huynh, Phat K.
AU - Duc An Bui, Vinh
AU - Young Hwang, Kee
AU - Jain, Nityanand
AU - Nguyen, Chau
AU - Minh, Le Huu Nhat
AU - Truong, Le Van
AU - Nguyen, Xuan Thanh
AU - Nguyen, Dinh Hoang
AU - Dung, Le Tien
AU - Le, Trung Q.
AU - Phan, Manh-Huong
PY - 2023/11/1
Y1 - 2023/11/1
N2 - The COVID-19 pandemic underscored the importance of reliable, noninvasive diagnostic tools for robust public health interventions. In this work, we fused magnetic respiratory sensing technology (MRST) with machine learning (ML) to create a diagnostic platform for real-time tracking and diagnosis of COVID-19 and other respiratory diseases. The MRST precisely captures breathing patterns through three specific breath testing protocols: normal breath, holding breath, and deep breath. We collected breath data from both COVID-19 patients and healthy subjects in Vietnam using this platform, which then served to train and validate ML models. Our evaluation encompassed multiple ML algorithms, including support vector machines and deep learning models, assessing their ability to diagnose COVID-19. Our multi-model validation methodology ensures a thorough comparison and grants the adaptability to select the most optimal model, striking a balance between diagnostic precision with model interpretability. The findings highlight the exceptional potential of our diagnostic tool in pinpointing respiratory anomalies, achieving over 90% accuracy. This innovative sensor technology can be seamlessly integrated into healthcare settings for patient monitoring, marking a significant enhancement for the healthcare infrastructure.
AB - The COVID-19 pandemic underscored the importance of reliable, noninvasive diagnostic tools for robust public health interventions. In this work, we fused magnetic respiratory sensing technology (MRST) with machine learning (ML) to create a diagnostic platform for real-time tracking and diagnosis of COVID-19 and other respiratory diseases. The MRST precisely captures breathing patterns through three specific breath testing protocols: normal breath, holding breath, and deep breath. We collected breath data from both COVID-19 patients and healthy subjects in Vietnam using this platform, which then served to train and validate ML models. Our evaluation encompassed multiple ML algorithms, including support vector machines and deep learning models, assessing their ability to diagnose COVID-19. Our multi-model validation methodology ensures a thorough comparison and grants the adaptability to select the most optimal model, striking a balance between diagnostic precision with model interpretability. The findings highlight the exceptional potential of our diagnostic tool in pinpointing respiratory anomalies, achieving over 90% accuracy. This innovative sensor technology can be seamlessly integrated into healthcare settings for patient monitoring, marking a significant enhancement for the healthcare infrastructure.
KW - Magnetic Respiratory Sensor
KW - Machine Learning
KW - Real-time COVID-19 diagnosis
KW - Real-time COVID-19 monitoring
UR - https://www-scopus-com.db.rsu.lv/results/results.uri?st1=Real-Time+Magnetic+Tracking+and+Diagnosis+of+COVID-19+via+Machine+Learning&st2=&s=TITLE%28Real-Time+Magnetic+Tracking+and+Diagnosis+of+COVID-19+via+Machine+Learning%29&limit=10&origin=searchbasic&sort=plf-f&src=pp&sot=b&sdt=b&sessionSearchId=b98a70a2df8872c7ae97b2e43fc34438&yearFrom=2017&yearTo=Present
U2 - 10.48550/arXiv.2311.00737
DO - 10.48550/arXiv.2311.00737
M3 - Preprint
SP - 1
EP - 57
BT - Real-Time Magnetic Tracking and Diagnosis of COVID-19 via Machine Learning
PB - arXiv.org
CY - United States
ER -
