用语音特征预测帕金森病进展，准确率达95.6%。
Detection and Forecasting of Parkinson Disease Progression from Speech Signal Features Using MultiLayer Perceptron and LSTM
- 结合LSTM与MLP，利用语音诊断特征建模病情发展。
- 在174例患者数据上，阶段预测准确率95.6%，存在检测达98.3%。
- 适合临床早期干预和长期跟踪的辅助诊断系统研发者。
帕金森病的早期准确诊断颇具挑战。本研究利用机器学习技术，基于帕金森患者语音信号的诊断特征，分别训练长短期记忆网络（LSTM）预测疾病进展，多层感知机（MLP）用于疾病检测。采用Relief-F和顺序前向选择两种特征筛选方法提取关键特征，在包含174例患者的样本上，模型对疾病进展阶段（第2、3期）的预测准确率达95.6%，疾病存在性检测准确率为98.3%。结果表明，该方法可有效支持帕金森病的早期识别与动态追踪。
原文摘要 · Abstract (English)
Accurate diagnosis of Parkinson disease, especially in its early stages, can be a challenging task. The application of machine learning techniques helps improve the diagnostic accuracy of Parkinson disease detection but only few studies have presented work towards the prediction of disease progression. In this research work, Long Short Term Memory LSTM was trained using the diagnostic features on Parkinson patients speech signals, to predict the disease progression while a Multilayer Perceptron MLP was trained on the same diagnostic features to detect the disease. Diagnostic features selected using two well-known feature selection methods named Relief-F and Sequential Forward Selection and applied on LSTM and MLP have shown to accurately predict the disease progression as stage 2 and 3 and its existence respectively.
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