Genetic justification of COVID-19 patient outcomes using DERGA, a novel data ensemble refinement greedy algorithm
- Authors: Asteris P.G.; Gandomi A.H.; Armaghani D.J.; Tsoukalas M.Z.; Gavriilaki E.; Gerber G.; Konstantakatos G.; Skentou A.D.; Triantafyllidis L.; Kotsiou N.; Braunstein E.; Chen H.; Brodsky R.; Touloumenidou T.; Sakellari I.; Alkayem N.F.; Bardhan A.; Cao M.; Cavaleri L.; Formisano A.; Guney D.; Hasanipanah M.; Khandelwal M.; Mohammed A.S.; Samui P.; Zhou J.; Terpos E.; Dimopoulos M.A.
- Publication year: 2024
- Type: Articolo in rivista
- OA Link: http://hdl.handle.net/10447/665323
Abstract
Complement 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.