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

TL;DR

This study employs a neural network with feature selection to accurately diagnose and predict COVID-19 severity using routine blood tests, potentially aiding healthcare management and remote monitoring.

Contribution

It introduces a LogNNet neural network model with backward feature elimination for effective COVID-19 diagnosis and prognosis based on routine blood values.

Findings

Achieved 99.5% accuracy in COVID-19 diagnosis with 46 features.

Achieved 94.4% accuracy in prognosis with 48 features.

Identified key blood features for diagnosis and prognosis.

Abstract

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.