Enhancing Parkinson's disease severity assessment through voice-based wavelet scattering,optimized model selection,and weighted majority voting

作     者：Farhad Abedinzadeh Torghabeh Seyyed Abed Hosseini Elham Ahmadi Moghadam 

作者机构：Department of Biomedical EngineeringMashhad BranchIslamic Azad UniversityMashhadIran Department of Electrical EngineeringMashhad BranchIslamic Azad UniversityMashhadIran 

出 版 物：《Medicine in Novel Technology and Devices》 (医学中新技术与新装备（英文）)

年 卷 期：2023年第20卷第4期

页      面：51-63页

学科分类：1002[医学-临床医学] 100203[医学-老年医学] 10[医学] 

主　　题：Parkinson's disease Speech impairment Voice activity detection Model selection Bayesian optimization Weighted majority voting 

摘      要：Parkinson s disease(PD)is a neurodegenerative disorder characterized by motor and non-motor symptoms that significantly impact an individual s quality of *** changes have shown promise as early indicators of PD,making voice analysis a valuable tool for early detection and *** study aims to assess and detect the severity of PD through voice analysis using the mobile device voice recordings *** dataset consisted of recordings from PD patients at different stages of the disease and healthy control subjects.A novel approach was employed,incorporating a voice activity detection algorithm for speech segmentation and the wavelet scattering transform for feature extraction.A Bayesian optimization technique is used to fine-tune the hyperparameters of seven commonly used classifiers and optimize the performance of machine learning classifiers for PD severity *** and K-nearest neighbor consistently demonstrated superior performance across various evaluation metrics among the ***,a weighted majority voting(WMV)technique is implemented,leveraging the predictions of multiple models to achieve a near-perfect accuracy of 98.62%,improving classification *** results highlight the promising potential of voice analysis in PD diagnosis and *** advanced signal processing techniques and machine learning models provides reliable and accessible tools for PD assessment,facilitating early intervention and improving patient *** study contributes to the field by demonstrating the effectiveness of the proposed methodology and the significant role of WMV in enhancing classification accuracy for PD severity detection.