Precision in Contactless Respiratory Monitoring: Machine Learning-Based Algorithm Performance

Respiratory monitoring is crucial for patient care, especially for infants, providing essential data for diagnosing and managing various health conditions. Traditional methods often lack the precision and real-time capabilities required for optimal outcomes. This study evaluates the performance of machine learning (ML) based algorithms for the automation of contactless respiration monitoring to reduce the manual intervention of medical experts in predicting the health condition of neonates in the neonatal intensive care unit (NICU). Particularly, the random forest algorithm is used in this work for enhancing precision in respiratory monitoring. This algorithm is known for its robustness and ability to handle complex, non-linear relationships and aggregate predictions from multiple decision trees to improve accuracy. This research involves analyzing extensive patient data, especially respiratory rate, to train and validate the algorithm. Preliminary results indicate that the proposed ML-based algorithm significantly improves the precision of respiratory monitoring and offers a promising tool for healthcare providers. This study highlights the potential for machine learning to transform patient care monitoring.