用多模态数据和量子启发模型，90%准确率预测帕金森病。
A quantum inspired predictor of Parkinsons disease built on a diverse, multimodal dataset
- 基于语音、步态等4类生物标志物构建特征集
- 在15万样本上实现90%准确率与0.98 AUC
- 可运行于普通硬件的量子模拟分类框架
帕金森病是全球增长最快的神经退行性疾病，两年内病例数增加50%。随着言语、记忆和运动症状恶化，早期诊断对提升患者生活质量至关重要。尽管基于机器学习的检测已显潜力，但单一特征分类易受个体症状差异影响。为此，我们采用mPower数据库，包含15万份样本及语音、步态、敲击和人口统计学四类关键生物标志物。从中提取64个特征，并用随机森林筛选出超过80百分位的特征。针对分类，设计了可模拟的量子支持向量机（qSVM），利用量子机器学习最新进展捕捉高维模式。该新型可模拟架构可在标准硬件上运行，无需依赖资源密集型量子计算机，最终实现90%准确率与0.98 AUC，优于基准模型。通过融合多样化特征的创新分类框架，为全球可及的帕金森病筛查提供可行路径。
原文摘要 · Abstract (English)
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.
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