  Healthcare digitalization requires effective applications of human sensors,
when various parameters of the human body are instantly monitored in everyday
life due to the Internet of Things (IoT). In particular, machine learning (ML)
sensors for the prompt diagnosis of COVID-19 are an important option for IoT
application in healthcare and ambient assisted living (AAL). Determining a
COVID-19 infected status with various diagnostic tests and imaging results is
costly and time-consuming. This study provides a fast, reliable and
cost-effective alternative tool for the diagnosis of COVID-19 based on the
routine blood values (RBVs) measured at admission. The dataset of the study
consists of a total of 5296 patients with the same number of negative and
positive COVID-19 test results and 51 routine blood values. In this study, 13
popular classifier machine learning models and the LogNNet neural network model
were exanimated. The most successful classifier model in terms of time and
accuracy in the detection of the disease was the histogram-based gradient
boosting (HGB) (accuracy: 100%, time: 6.39 sec). The HGB classifier identified
the 11 most important features (LDL, cholesterol, HDL-C, MCHC, triglyceride,
amylase, UA, LDH, CK-MB, ALP and MCH) to detect the disease with 100% accuracy.
In addition, the importance of single, double and triple combinations of these
features in the diagnosis of the disease was discussed. We propose to use these
11 features and their binary combinations as important biomarkers for ML
sensors in the diagnosis of the disease, supporting edge computing on Arduino
and cloud IoT service.
