# Predicting Onset of Dementia in Parkinson's Disease Patients

## TL;DR

This study develops a machine learning approach that integrates clinical, imaging, genetic, and biospecimen data to predict dementia onset in Parkinson's disease patients, highlighting the overlap between AD and PD.

## Contribution

It introduces a joint feature space for AD and PD, improving prediction accuracy of dementia onset through combined training on both diseases.

## Key findings

- Enhanced prediction accuracy for Parkinson's dementia.
- Joint analysis supports overlapping disease pathways.
- Machine learning classifier effectively predicts disease progression.

## Abstract

Alzheimer's disease (AD) and Parkinson's disease (PD) are the two most common neurodegenerative disorders in humans. Because a significant percentage of patients have clinical and pathological features of both diseases, it has been hypothesized that the patho-cascades of the two diseases overlap. Despite this evidence, these two diseases are rarely studied in a joint manner. In this paper, we utilize clinical, imaging, genetic, and biospecimen features to cluster AD and PD patients into the same feature space. By training a machine learning classifier on the combined feature space, we predict the disease stage of patients two years after their baseline visits. We observed a considerable improvement in the prediction accuracy of Parkinson's dementia patients due to combined training on Alzheimer's and Parkinson's patients, thereby affirming the claim that these two diseases can be jointly studied.