# Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application

arXiv: https://arxiv.org/abs/2209.03522 · HTML: https://mlsanity.com/paper/2209.03522/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 125 references that are in the map.
- First posted on arXiv 8 Sep 2022.

## Builds on (most cited first, 7 of 7)

- [Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning](https://mlsanity.com/paper/2007.04074/) (2020)
- [Neural Network for Low-Memory IoT Devices and MNIST Image Recognition Using Kernels Based on Logistic Map](https://mlsanity.com/paper/2006.02824/) (2020)
- [COVID-19 diagnosis by routine blood tests using machine learning](https://mlsanity.com/paper/2006.03476/) (2020)
- [A Method for Estimating the Entropy of Time Series Using Artificial Neural Networks](https://mlsanity.com/paper/2107.08399/) (2021)
- [Machine Learning Sensors](https://mlsanity.com/paper/2206.03266/) (2022)
- [A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare](https://mlsanity.com/paper/2108.02428/) (2021)
- [Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network](https://mlsanity.com/paper/2205.09974/) (2022)

## Built on by (most cited first, 3 of 3)

- [Detection of Risk Predictors of COVID-19 Mortality with Classifier Machine Learning Models Operated with Routine Laboratory Biomarkers](https://mlsanity.com/paper/2210.12342/) (2022)
- [Entropy-based machine learning model for diagnosis and monitoring of Parkinson's Disease in smart IoT environment](https://mlsanity.com/paper/2309.07134/) (2023)
- [Evaluating COVID 19 Feature Contributions to Bitcoin Return Forecasting: Methodology Based on LightGBM and Genetic Optimization](https://mlsanity.com/paper/2508.00078/) (2025)

## Abstract (arXiv)

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.