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

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 84% accuracy across the entire cohort, 86% in patients with negative CSF, and 88% in individuals with AD-like CSF. SHAP analysis identified differences between CSF-positive and -negative patients in predictors of conversion and cut-offs.

CONCLUSIONS

The approach yielded good prediction accuracy using SCD. However, CSF-based categorizations are needed to improve predictive accuracy.

HIGHLIGHTS

Machine learning algorithms can predict cognitive decline with standard and routinely used clinical data. Classification according to cerebrospinal fluid biomarkers enhances prediction accuracy. Different cut-offs could be applied to neuropsychological tests to predict conversion.