Genetic justification of COVID‐19 patient outcomes using DERGA, a novel data ensemble refinement greedy algorithm

Author:

Asteris Panagiotis G.1ORCID,Gandomi Amir H.23,Armaghani Danial J.4,Tsoukalas Markos Z.1,Gavriilaki Eleni5ORCID,Gerber Gloria6,Konstantakatos Gerasimos1,Skentou Athanasia D.1,Triantafyllidis Leonidas1,Kotsiou Nikolaos5ORCID,Braunstein Evan6,Chen Hang6,Brodsky Robert6,Touloumenidou Tasoula7,Sakellari Ioanna7,Alkayem Nizar Faisal8,Bardhan Abidhan9,Cao Maosen10,Cavaleri Liborio11,Formisano Antonio12,Guney Deniz13,Hasanipanah Mahdi14,Khandelwal Manoj15ORCID,Mohammed Ahmed Salih16,Samui Pijush9,Zhou Jian17,Terpos Evangelos18,Dimopoulos Meletios A.18

Affiliation:

1. Computational Mechanics Laboratory, School of Pedagogical and Technological Education Athens Greece

2. Faculty of Engineering & IT University of Technology Sydney Sydney New South Wales Australia

3. University Research and Innovation Center (EKIK), Óbuda University Budapest Hungary

4. School of Civil and Environmental Engineering University of Technology Sydney Sydney New South Wales Australia

5. 2nd Propedeutic Department of Internal Medicine Aristotle University of Thessaloniki Thessaloniki Greece

6. Hematology Division Johns Hopkins University Baltimore USA

7. Hematology Department – BMT Unit G Papanicolaou Hospital Thessaloniki Greece

8. College of Civil and Transportation Engineering Hohai University Nanjing China

9. Civil Engineering Department National Institute of Technology Patna Patna India

10. Department of Engineering Mechanics Hohai University Nanjing China

11. Department of Civil, Environmental, Aerospace and Materials Engineering University of Palermo Palermo Italy

12. Department of Structures for Engineering and Architecture University of Naples “Federico II” Naples Italy

13. Engineering Faculty San Diego State University San Diego California USA

14. Department of Geotechnics and Transportation, Faculty of Civil Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia

15. Institute of Innovation, Science and Sustainability Federation University Australia Ballarat Victoria Australia

16. Engineering Department American University of Iraq Sulaymaniyah Iraq

17. School of Resources and Safety Engineering Central South University Changsha China

18. Department of Clinical Therapeutics, Medical School, Faculty of Medicine National Kapodistrian University of Athens Athens Greece

Abstract

AbstractComplement inhibition has shown promise in various disorders, including COVID‐19. A prediction tool including complement genetic variants is vital. This study aims to identify crucial complement‐related variants and determine an optimal pattern for accurate disease outcome prediction. Genetic data from 204 COVID‐19 patients hospitalized between April 2020 and April 2021 at three referral centres were analysed using an artificial intelligence‐based algorithm to predict disease outcome (ICU vs. non‐ICU admission). A recently introduced alpha‐index identified the 30 most predictive genetic variants. DERGA algorithm, which employs multiple classification algorithms, determined the optimal pattern of these key variants, resulting in 97% accuracy for predicting disease outcome. Individual variations ranged from 40 to 161 variants per patient, with 977 total variants detected. This study demonstrates the utility of alpha‐index in ranking a substantial number of genetic variants. This approach enables the implementation of well‐established classification algorithms that effectively determine the relevance of genetic variants in predicting outcomes with high accuracy.

Publisher

Wiley

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