Personalized COVID-19 Early Detection Using Wearable Data Based on Self-Reported Records

The use of wearable technology for early disease detection has gained traction as a promising avenue for improving public health outcomes. This study investigates the potential of wearable devices in early COVID-19 detection through a comprehensive methodology that integrates Long Short-Term Memory (LSTM) networks, personalized fine-tuning, and the clean-lab framework for label noise correction. Leveraging the resting heart rate (RHR) data collected from wearable devices, the study demonstrates how personalized fine-tuning improves model performance by adapting to unique physiological baselines of individuals. The incorporation of the clean-lab framework rectifies label inconsistencies arising from self-reported data inaccuracies, further enhancing model accuracy. Results indicate that the combination of personalized fine-tuning and label noise correction yields the most significant performance improvements, with heightened sensitivity, specificity, and F1 scores. The findings highlight the importance of individualized approaches and data quality control mechanisms in harnessing the potential of wearable technology for early disease detection. By addressing challenges related to variability and data quality, wearable technology may emerge as a powerful tool for timely disease identification and intervention.