Machine learning-based prediction of mild cognitive impairment among individuals with normal cognitive function.

ABSTRACT:

Background

Previous studies mainly focused on risk factors in patients with mild cognitive impairment (MCI) or dementia. The aim of the study was to provide basis for preventing MCI in cognitive normal populations.

Methods

The data came from a longitudinal retrospective study involving individuals with brain magnetic resonance imaging scans, clinical visits, and cognitive assessment with interval of more than 3 years. Multiple machine-learning technologies, including random forest, support vector machine, logistic regression, eXtreme Gradient Boosting, and naïve Bayes, were used to establish a prediction model of a future risk of MCI through a combination of clinical and image variables.

Results

Among these machine learning models; eXtreme Gradient Boosting (XGB) was

SUBMITTER: Zhu XW 

PROVIDER: S-EPMC10870793 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature