Machine learning diagnosis of cognitive impairment and dementia in harmonized older adult cohorts.

ABSTRACT:

Introduction

Clinical diagnosis (normal cognition, mild cognitive impairment [MCI], dementia) is critical for understanding cognitive impairment and dementia but can be resource intensive and subject to inconsistencies due to complex clinical judgments that are required. Machine learning approaches might provide meaningful additions and/or alternatives to traditional clinical diagnosis.

Methods

The study sample was composed of three harmonized longitudinal cohorts of demographically diverse older adults. We used the XGBoost extreme gradient boosting platform to predict clinical diagnosis using different feature sets.

Results

Measures of cognition were especially important predictive features of clinical diagnosis. Prediction accuracy was higher in a sample that had lo

SUBMITTER: Mungas D 

PROVIDER: S-EPMC12314546 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature