Detection and Forecasting of Parkinson Disease Progression from Speech Signal Features Using MultiLayer Perceptron and LSTM

TL;DR

This study employs LSTM and MLP neural networks trained on speech features to detect Parkinson's disease and predict its progression stages, enhancing early diagnosis and monitoring accuracy.

Contribution

It introduces a combined approach using LSTM and MLP with feature selection methods for disease detection and progression prediction.

Findings

LSTM accurately predicts disease progression stages 2 and 3.

MLP effectively detects the presence of Parkinson's disease.

Feature selection improves model performance.

Abstract

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.