Helicobacter pylori (H. pylori) risk factor analysis and prevalence prediction: a machine learning-based approach.

ABSTRACT:

Background

Although previous epidemiological studies have examined the potential risk factors that increase the likelihood of acquiring Helicobacter pylori infections, most of these analyses have utilized conventional statistical models, including logistic regression, and have not benefited from advanced machine learning techniques.

Objective

We examined H. pylori infection risk factors among school children using machine learning algorithms to identify important risk factors as well as to determine whether machine learning can be used to predict H. pylori infection status.

Methods

We applied feature selection and classification algorithms to data from a school-based cross-sectional survey in Ethiopia. The data set included 954 school children with 27 sociodemographic

SUBMITTER: Tran V 

PROVIDER: S-EPMC9330977 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature