Detection and Forecasting of Parkinson Disease Progression from Speech Signal Features Using Multi-Layer Perceptron and LSTM

Abstract

Accurate diagnosis of Parkinson disease, especially in its early stages, can be a challenging task. The
application of machine learning techniques help improve the diagnostic accuracy of Parkinson’s disease
progression. In this research work, two well-known feature selection methods (Relief-F and Sequential Forward
Selection) were employed to identify the diagnostic features of audio signals of Parkinson disease patients and
were used to train Multi-Layer Perceptron (MLP) and recurrent neural network Long Short-Term Memory(LSTM) for
detection of disease and prediction of its progression. The MLP accurately detected Parkinson disease stages
whereas LSTM successfully predicted Parkinson Stage 2 and 3.