Machine Learning in Commercial Wearable Devices for the Quantification of the Performance of COVID-19 Diagnosis: A Review

The rapid spread of COVID-19 has resulted in more than 635 million cases worldwide, highlighting the need for efficient, noninvasive, and cost-effective diagnostic methods. Traditional diagnostic methods such as RT-PCR are effective but often invasive and slow. This systematic review, following Preferred Reporting Items for Systematic reviews and Meta-Analyses PRISMA guidelines evaluates studies using machine learning techniques on physiological data from wearable devices for COVID-19 diagnosis. We included 20 peer-reviewed articles, conference papers, and preprints published between November 2020 and January 2024, focusing on studies using physiological parameters collected through commercial wearable devices and machine learning for COVID-19 diagnosis. An analysis of the variables used and feature extraction is presented, highlighting the time-domain heart rate, and statistically due to Heart Rate Variability HRV. The most frequently used Machine Learning ML algorithms (Random Forest, Support Vector Machine, Logistic Regression, and k-Nearest Neighbor) in this context are also shown, as well as their performance analyzed by variable, algorithm, features used, and study population. The 20 included studies reported Appropriate Use Criteria area under the curve AUC values ranging from 0.770 to 0.994, indicating high diagnostic accuracy. The meta-analysis showed a high level of heterogeneity ( I 2 = 84%) across studies. This review highlights the potential of combining commercially available wearable technology withmachine learning for early and accurate detection of COVID-19, suggesting directions for future research to improve noninvasive diagnostic methods and suggest improvements in the reporting of results.