Use of artificial intelligence to assess genetic predisposition to develop critical COVID-19 disease: a comparative study of machine learning models.

ABSTRACT:

Objectives

Early prediction of critical COVID-19 disease is crucial for an optimal clinical management. The objective of this study was to optimize predictive models for critical COVID-19 disease. Clinical data, laboratory data and genetic polymorphisms were integrated into AI models to compare the performance of different machine learning algorithms.

Methods

Data from 155 inpatients were analyzed, 23 of whom developed critical disease. A univariate analysis was performed to assess potential correlations between seven SNPs, nine clinical variables and 10 laboratory parameters at admission.

Results

Of the 7 SNPs, only three SNPs demonstrated a significant association with critical disase, namely: rs77534576, rs10774671 and rs10490770. The ensemble models exhibited the

SUBMITTER: Martin Perez S 

PROVIDER: S-EPMC12107411 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature