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 longer follow-up, better balance across diagnostic outcomes, and both self- and informant-report independent function measures.

DISCUSSION

Algorithmic diagnosis might be a meaningful substitute for clinical diagnosis in studies in which clinical evaluation and diagnosis are not feasible for all participants and may provide a standardized alternative when clinical diagnosis is available.

Highlights

• A machine learning algorithm was used to diagnose cognitive impairment and dementia.
• Measures of cognition were strongest predictive features for clinical diagnosis.
• Algorithm accuracy was improved by informant-report independent function measures.
• Algorithmic diagnosis might be an alternative if clinical diagnosis is not feasible.
• Standardization is an important advantage of algorithmic diagnosis.