Development and validation of an interpretable machine learning model-Predicting mild cognitive impairment in a high-risk stroke population.

ABSTRACT:

Background

Mild cognitive impairment (MCI) is considered a preclinical stage of Alzheimer's disease (AD). People with MCI have a higher risk of developing dementia than healthy people. As one of the risk factors for MCI, stroke has been actively treated and intervened. Therefore, selecting the high-risk population of stroke as the research object and discovering the risk factors of MCI as early as possible can prevent the occurrence of MCI more effectively.

Methods

The Boruta algorithm was used to screen variables, and eight machine learning models were established and evaluated. The best performing models were used to assess variable importance and build an online risk calculator. Shapley additive explanation is used to explain the model.

Results

A total of 199 patie

SUBMITTER: Yan FJ 

PROVIDER: S-EPMC10308219 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature