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Covid-19 rapid test by combining a Random Forest-based web system and blood tests
Valter Augusto de Freitas Barbosa1, Juliana Carneiro Gomes2, Maíra Araújo de Santana2
1Federal University of Pernambuco, Recife, Brazil.
Insights
A new AI system, Heg.IA, aids in diagnosing COVID-19 and recommending hospitalization using 41 blood test parameters. This tool offers a potential solution for testing unavailability, improving public health responses to the SARS-CoV-2 pandemic.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
- Infectious Disease Diagnostics
Background:
- COVID-19, caused by SARS-CoV-2, presents significant global public health challenges.
- Over 2 million deaths underscore the urgent need for effective diagnostic and management tools.
- Limited testing availability can hinder timely diagnosis and patient care.
Purpose of the Study:
- To develop and evaluate Heg.IA, a web-based artificial intelligence solution for optimizing COVID-19 diagnosis.
- To assist healthcare professionals in decision-making regarding COVID-19 diagnosis and patient hospitalization levels (ward, semi-ICU, ICU).
Main Methods:
- Implementation of a Random Forest machine learning model with 90 decision trees.
- Input of 41 hematological and arterial gasometry parameters from routine blood tests.
- Development of a web solution for automated diagnostic reporting and hospitalization recommendations.
Main Results:
- Achieved high accuracy (92.89%) for COVID-19 diagnosis, with a kappa index of 0.858 and AUC of 0.984.
- Demonstrated excellent performance for hospitalization indication, with accuracies exceeding 99%.
- The computationally efficient AI model provides reliable diagnostic support.
Conclusions:
- The Heg.IA system effectively supports COVID-19 diagnosis and hospitalization decisions.
- This AI-driven approach offers a valuable alternative to overcome testing limitations.
- Heg.IA has the potential to enhance healthcare system efficiency during infectious disease outbreaks.
Abstract:
The disease caused by the new type of coronavirus, Covid-19, has posed major public health challenges for many countries. With its rapid spread, since the beginning of the outbreak in December 2019, the disease transmitted by SARS-CoV-2 has already caused over 2 million deaths to date. In this work, we propose a web solution, called Heg.IA, to optimize the diagnosis of Covid-19 through the use of artificial intelligence. Our system aims to support decision-making regarding to diagnosis of Covid-19 and to the indication of hospitalization on regular ward, semi-ICU or ICU based on decision a Random Forest architecture with 90 trees. The main idea is that healthcare professionals can insert 41 hematological parameters from common blood tests and arterial gasometry into the system. Then, Heg.IA will provide a diagnostic report. The system reached good results for both Covid-19 diagnosis and to recommend hospitalization. For the first scenario we found average results of accuracy of 92.891%±0.851, kappa index of 0.858 ± 0.017, sensitivity of 0.936 ± 0.011, precision of 0.923 ± 0.011, specificity of 0.921 ± 0.012 and area under ROC of 0.984 ± 0.003. As for the indication of hospitalization, we achieved excellent performance of accuracies above 99% and more than 0.99 for the other metrics in all situations. By using a computationally simple method, based on the classical decision trees, we were able to achieve high diagnosis performance. Heg.IA system may be a way to overcome the testing unavailability in the context of Covid-19.Communicated by Ramaswamy H. Sarma.
