ABSTRACT
Health is an indispensable part of human life, but we realize its importance when we face health issues. Technology can play an important role in the healthcare sector. During the COVID-19 pandemic, many countries used technology to control the situation. Internet of Things-based wearable devices can change the whole scenario of diagnosing the disease. The physiological features collected using wearables can be used for pre-symptomatic prediction of disease. In this study, from the cohort of 185 participants, data of 36 participants are analyzed to predict COVID-19 before symptoms begin using the machine learning model. Our findings suggest that heart rate, BPM, SDNN, and steps features can be used to detect the COVID-19 before the symptoms appear. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.