Work

A hybrid computational framework for Parkinson’s disease prediction using fractional-order modeling and machine learning via vocal biomarkers

Year: 2025

Type: article

Abstract: This study introduces a hybrid approach combining fractional-order dynamical modeling with machine learning (ML) to predict Parkinson’s disease (PD) using vocal biomarkers. A fractional-order system captures memory-dependent vocal impairment dynamics, with proven stability and uniqueness. Calibrated with clinical data, it achieves 97.44 % accuracy, while a Deep Neural Network (DNN) attains 91.8 % accuracy (AUC: 95.8 %). Key biomarker Pitch Period Entropy (PPE) (importance: 0.9014) emerges as most discriminative. The fusion of fractional calculus and ML enhances interpretability and predictive power, enabling early, non-invasive PD detection. This framework advances computational neurology, offering potential for personalized monitoring and improved diagnostic tools.

Source: Ain Shams Engineering Journal

DOI: 10.1016/j.asej.2025.103889

Authors: David Amilo, Khadijeh Sadri, Evren Hincal, Mohamed Hafez

Institutions: Near East University, Khazar University, Shinawatra University, Nilai University

Language: English

Topic: Voice and Speech Disorders

Subfield: Physiology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,200