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1 Computer Science

1 2025

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Detection and forecasting of parkinson disease progression from speech signal features using multilayer perceptron and lSTM

Ali Majid, Shakir Hina, Samreen Asia, Ahmed Sohaib

Computer Science

2025

T. 26 (2)

Accurate diagnosis of P arkinson′sdisease, especially in its early stages, can bea challenging task. The application of machine learning (ML) techniques hashelped improve the diagnostic accuracy of P arkinson′sdisease (PD) detectionbut integration of diagnostic features in ML models for the prediction of disease progression has remained an unexplored research avenue. In this researchwork, Long Short Term Memory (LSTM) was trained using diagnostic features on Parkinson patients speech signals, to predict the disease progression whilea Multilayer Perceptron (MLP) was trained on the same diagnostic features to detect PD. Diagnostic features were selected using two well known features election methods named Relief F and Sequential Forward Selection method. The integration of feature selection methods in LSTM model has resulted in PD progression forecast with an accuracy of 88.7%. Further more, with the application of input diagnostic features on MLP, PD stage was accurately detected with an accuracy of 98.63%, precision of 97.64% and recall of 98.8% showing model robustness and efficiency for its potential application in health care.