Predicting conversion in cognitively normal and mild cognitive impairment individuals with machine learning: Is the CSF status still relevant?

ABSTRACT:

Introduction

Machine learning (ML) helps diagnose the mild cognitive impairment-Alzheimer's disease (MCI-AD) spectrum. However, ML is fed with data unavailable in standard clinical practice. Thus, we tested a novel multi-step ML approach to predict cognitive worsening.

Methods

We selected cognitively normal and MCI participants from the Alzheimer's Disease Neuroimaging Initiative dataset and categorized them on total tau/amyloid beta 1-42 ratios. ML was applied to predict the 3-year conversion with standard clinical data (SCD), assess the model's accuracy, and identify the role of cerebrospinal fluid (CSF) biomarkers in this approach. Shapley Additive Explanations (SHAP) analysis was carried out to explore the automated decisional process.

Results

The model achieved 8

SUBMITTER: Russo M 

PROVIDER: S-EPMC11848327 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature