Using machine learning to improve our understanding of COVID-19 infection in children.

ABSTRACT:

Purpose

Children are at elevated risk for COVID-19 (SARS-CoV-2) infection due to their social behaviors. The purpose of this study was to determine if usage of radiological chest X-rays impressions can help predict whether a young adult has COVID-19 infection or not.

Methods

A total of 2572 chest impressions from 721 individuals under the age of 18 years were considered for this study. An ensemble learning method, Random Forest Classifier (RFC), was used for classification of patients suffering from infection.

Results

Five RFC models were implemented with incremental features and the best model achieved an F1-score of 0.79 with Area Under the ROC curve as 0.85 using all input features. Hyper parameter tuning and cross validation was performed using grid search cross v

SUBMITTER: Piparia S 

PROVIDER: S-EPMC9931095 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature