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.

翻译：帕金森病的准确诊断，尤其在早期阶段，可能是一项具有挑战性的任务。机器学习技术的应用有助于提升帕金森病检测的诊断准确性，但仅有少数研究致力于预测疾病进展。在本研究工作中，我们利用帕金森病患者语音信号的诊断特征训练长短期记忆网络（LSTM）以预测疾病进展，同时使用相同的诊断特征训练多层感知机（MLP）以检测疾病。通过两种知名特征选择方法——Relief-F与顺序前向选择——筛选出的诊断特征，被应用于LSTM与MLP模型，结果显示其能分别准确预测疾病进展至第2、3阶段以及疾病的存在。