Abstract
Parkinson's disease, currently the fastest-growing neurodegenerative disorder globally, has seen a 50% increase in cases within just two years. As disease progression impairs speech, memory, and motor functions over time, early diagnosis is crucial for preserving patients' quality of life. Although machine-learning-based detection has shown promise for detecting Parkinson's disease, most studies rely on a single feature for classification and can be error-prone due to the variability of symptoms between patients. To address this limitation we utilized the mPower dataset, 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 this novel and simulatable architecture that can be run on standard hardware rather than resource-intensive quantum computers, our model achieves an accuracy of 90%, F-1 score of 0.90, and an AUC of 0.98-surpassing benchmark models. Utilizing an innovative classification framework built on a diverse set of features, our model offers a pathway for accessible global Parkinson's screening.