Early prediction of cognitive impairment in adults aged 20 years and older using machine learning and biomarkers of heavy metal exposure.

ABSTRACT:

Background

Cognitive impairment poses a growing health challenge as populations age. Heavy metals are implicated as environmental risk factors, but their role is not fully understood. Machine learning can integrate multi-factorial data to predict cognitive outcomes.

Objective

To develop and validate machine learning models for early prediction of cognitive impairment risk using demographics, clinical factors, and biomarkers of heavy metal exposure.

Method

A retrospective analysis was conducted using 2011-2014 NHANES data. Participants aged ≥ 20 underwent cognitive testing. Variables included demographics, medical history, lifestyle factors, and blood and urine levels of lead, cadmium, manganese, and other metals. Machine learning algorithms were trained on 90 % of dat

SUBMITTER: Nabavi A 

PROVIDER: S-EPMC11533558 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature