Real-Time Magnetic Tracking and Diagnosis of COVID-19 via Machine
  Learning
Dang Nguyen Phat K. Huynh Vinh Duc An Bui Kee Young Hwang Nityanand Jain Chau Nguyen Le Huu Nhat Minh Le Van Truong Xuan Thanh Nguyen Dinh Hoang Nguyen Le Tien Dung Trung Q. Le Manh-Huong Phan
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.