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

• Autorzy: Ali Majid , Shakir Hina , Samreen Asia , Ahmed Sohaib

Identyfikatory

DOI

10.7494/csci.2025.26.2.6691

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

Parkinson disease; machine learning; MLP; diagnostic features; LSTM

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2025
• Tom: T. 26 (2)
• Opis fizyczny: Bibliogr. 27 poz., rys., tab., wykr.

Twórcy

autor

Ali Majid

• Department of Software Engineering, Bahria University Karachi Pakistan

autor

Shakir Hina

• Department of Software Engineering, Bahria University Karachi Pakistan

autor

Samreen Asia

• Department of Software Engineering, Bahria University Karachi Pakistan

autor

Ahmed Sohaib

• Department of Software Engineering, Bahria University Karachi Pakistan