Heg.IA: an intelligent system to support diagnosis of Covid-19 based on blood tests

• Published in: Research on biomedical engineering Vol. 38; no. 1; pp. 99 - 116
• Main Authors: de Freitas Barbosa, Valter Augusto, Gomes, Juliana Carneiro, de Santana, Maíra Araújo, Albuquerque, Jeniffer E. de A., de Souza, Rodrigo Gomes, de Souza, Ricardo Emmanuel, dos Santos, Wellington Pinheiro
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.03.2022
• Subjects: Biomaterials; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; Engineering; Original; Original Article; Covid-19 rapid test; Software-based rapid test; Covid-19; Machine learning for diagnosis; Computer-aided diagnosis; Blood tests
• ISSN: 2446-4732; 2446-4740
• DOI: 10.1007/s42600-020-00112-5

Purpose A new kind of coronavirus, the SARS-CoV-2, started the biggest pandemic of the century. More than a million people have been killed by Covid-19. Because of this, quick and precise diagnosis test is necessary. The current gold standard is the RT-PCR with DNA sequencing and identification, but its results take too long to be available. Tests base on IgM/IgG antibodies have been used, but their sensitivity and specificity may be very low. Many studies have been demonstrating the Covid-19 impact on hematological parameters. Method This work proposes an intelligent system to support Covid-19 diagnosis based on blood testing. Laboratory parameters obtained from the hemogram and biochemical tests defined as standards to support clinical diagnosis were used as input features. Afterward, we used particle swarm optimization, evolutionary algorithms, and manual selection based on cost minimization to select the most significant features. Results We tested several machine learning methods, and we achieved high classification performance: overall accuracy of 95.159% ± 0.693, kappa index of 0.903 ± 0.014, sensitivity of 0.968 ± 0.007, precision of 0.938 ± 0.010, and specificity of 0.936 ± 0.011. These results were achieved using classical and low computational cost classifiers, with Bayes Network being the best of them. In addition, only 24 blood tests were needed. Conclusion This points to the possibility of a new rapid test with low cost. The desktop version of the system is fully functional and available for free use.