Using Machine Learning to Predict the Diagnosis, Management and Severity of Pediatric Appendicitis.

ABSTRACT: Background: Given the absence of consolidated and standardized international guidelines for managing pediatric appendicitis and the few strictly data-driven studies in this specific, we investigated the use of machine learning (ML) classifiers for predicting the diagnosis, management and severity of appendicitis in children. Materials and Methods: Predictive models were developed and validated on a dataset acquired from 430 children and adolescents aged 0-18 years, based on a range of information encompassing history, clinical examination, laboratory parameters, and abdominal ultrasonography. Logistic regression, random forests, and gradient boosting machines were used for predicting the three target variables. Results: A random forest classifier achieved areas under t

SUBMITTER: Marcinkevics R 

PROVIDER: S-EPMC8116489 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature