Machine learning-based risk factor analysis and prevalence prediction of intestinal parasitic infections using epidemiological survey data.

ABSTRACT:

Background

Previous epidemiological studies have examined the prevalence and risk factors for a variety of parasitic illnesses, including protozoan and soil-transmitted helminth (STH, e.g., hookworms and roundworms) infections. Despite advancements in machine learning for data analysis, the majority of these studies use traditional logistic regression to identify significant risk factors.

Methods

In this study, we used data from a survey of 54 risk factors for intestinal parasitosis in 954 Ethiopian school children. We investigated whether machine learning approaches can supplement traditional logistic regression in identifying intestinal parasite infection risk factors. We used feature selection methods such as InfoGain (IG), ReliefF (ReF), Joint Mutual Information (JMI), a

SUBMITTER: Zafar A 

PROVIDER: S-EPMC9236253 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature