A quantum inspired predictor of Parkinsons disease built on a diverse, multimodal dataset

TL;DR

This paper introduces a quantum-inspired machine learning model that combines diverse biomarkers to improve early Parkinson's disease detection, achieving high accuracy with a simulatable quantum support vector machine.

Contribution

It presents a novel, simulatable quantum support vector machine architecture that effectively integrates multimodal data for Parkinson's diagnosis, surpassing existing benchmarks.

Findings

Achieved 90% accuracy in Parkinson's detection

Attained an AUC of 0.98, outperforming traditional models

Demonstrated the feasibility of quantum-inspired models on standard hardware

Abstract

Parkinsons disease, the fastest growing neurodegenerative disorder globally, has seen a 50 percent increase in cases within just two years. As speech, memory, and motor symptoms worsen over time, early diagnosis is crucial for preserving patients quality of life. While machine-learning-based detection has shown promise, relying on a single feature for classification can be error-prone due to the variability of symptoms between patients. To address this limitation we utilized the mPower database, which includes 150,000 samples across four key biomarkers: voice, gait, tapping, and demographic data. From these measurements, we extracted 64 features and trained a baseline Random Forest model to select the features above the 80th percentile. For classification, we designed a simulatable quantum support vector machine (qSVM) that detects high-dimensional patterns, leveraging recent advancements in quantum machine learning. With a novel, simulatable architecture that can be run on standard hardware rather than resource-intensive quantum computers, our model achieves an accuracy of 90 percent and an AUC of 0.98, surpassing benchmark models. By utilizing an innovative classification framework built on a diverse set of features, our model offers a pathway for accessible global Parkinsons screening.