# Data-driven clinical decision support tool for diagnosing mild cognitive impairment in Parkinson’s disease

## TL;DR

A machine learning tool helps diagnose mild cognitive impairment in Parkinson’s disease patients more effectively than traditional methods.

## Contribution

A data-driven model using machine learning complements clinical assessments for diagnosing MCI in Parkinson’s disease.

## Key findings

- The ML model performed non-inferiorly to the clinical diagnostic test for MCI in Parkinson’s disease.
- The model identified an MCI subgroup missed by the clinical test, suggesting added diagnostic value.

## Abstract

Parkinson’s disease (PD) is a neurodegenerative condition that may affect both motor and cognitive function. Mild cognitive impairment (MCI) is a known risk factor for the progression to dementia in the later stages of the disease. Lengthy and time-consuming neuropsychological assessments, by trained experts, often make MCI diagnosis impractical in routine care. In this context, machine learning (ML) may offer promising support for MCI diagnosis. Thus, we analysed longitudinal data from 115 people with Parkinson’s disease (PwPD) and 226 healthy control participants from the Luxembourg Parkinson’s Study, combining ML with clinical data to support MCI diagnosis in PwPD. The data-driven model showed a non-inferior performance to the clinical diagnostic reference test (MDS PD-MCI Level II) and identified a subgroup of MCI individuals that was not captured by the clinical test. This finding suggests that ML models can complement clinical assessments, by facilitating the detection of MCI and complementing the diagnostic characterisation of PwPD.