# Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network

arXiv: https://arxiv.org/abs/2205.09974 · HTML: https://mlsanity.com/paper/2205.09974/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 76 references that are in the map.
- First posted on arXiv 20 May 2022.

## Builds on (most cited first, 6 of 6)

- [Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift](https://mlsanity.com/paper/1810.11953/) (2018)
- [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)
- [An improved LogNNet classifier for IoT application](https://mlsanity.com/paper/2105.14412/) (2021)
- [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)

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

- [Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application](https://mlsanity.com/paper/2209.03522/) (2022)
- [Neural Network Entropy (NNetEn): Entropy-Based EEG Signal and Chaotic Time Series Classification, Python Package for NNetEn Calculation](https://mlsanity.com/paper/2303.17995/) (2023)
- [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)

## Abstract (arXiv)

Since February 2020, the world has been engaged in an intense struggle with the COVID-19 dis-ease, and health systems have come under tragic pressure as the disease turned into a pandemic. The aim of this study is to obtain the most effective routine blood values (RBV) in the diagnosis and prognosis of COVID-19 using a backward feature elimination algorithm for the LogNNet reservoir neural network. The first dataset in the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 tests. The LogNNet-model achieved the accuracy rate of 99.5% in the diagnosis of the disease with 46 features and the accuracy of 99.17% with only mean corpuscular hemoglobin concentration, mean corpuscular hemoglobin, and activated partial prothrombin time. The second dataset consists of a total of 3899 patients with a diagnosis of COVID-19 who were treated in hospital, of which 203 were severe patients and 3696 were mild patients. The model reached the accuracy rate of 94.4% in determining the prognosis of the disease with 48 features and the accuracy of 82.7% with only erythrocyte sedimentation rate, neutrophil count, and C reactive protein features. Our method will reduce the negative pressures on the health sector and help doctors to understand the pathogenesis of COVID-19 using the key features. The method is promising to create mobile health monitoring systems in the Internet of Things.