Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - PD-ADSV
T2 - An automated diagnosing system using voice signals and hard voting ensemble method for Parkinson's disease[Formula presented]
AU - Ghaheri, Paria
AU - Shateri, Ahmadreza
AU - Nasiri, Hamid
PY - 2023/5/31
Y1 - 2023/5/31
N2 - Parkinson's disease (PD) is the most widespread movement condition and the second most common neurodegenerative disorder, following Alzheimer's. Movement symptoms and imaging techniques are the most popular ways to diagnose this disease. However, they are not accurate and fast and may only be accessible to a few people. This study provides an autonomous system, i.e., PD-ADSV, for diagnosing PD based on voice signals, which uses four machine learning classifiers and the hard voting ensemble method to achieve the highest accuracy. PD-ADSV is developed using Python and the Gradio web framework.
AB - Parkinson's disease (PD) is the most widespread movement condition and the second most common neurodegenerative disorder, following Alzheimer's. Movement symptoms and imaging techniques are the most popular ways to diagnose this disease. However, they are not accurate and fast and may only be accessible to a few people. This study provides an autonomous system, i.e., PD-ADSV, for diagnosing PD based on voice signals, which uses four machine learning classifiers and the hard voting ensemble method to achieve the highest accuracy. PD-ADSV is developed using Python and the Gradio web framework.
KW - Gradient Boosting
KW - LightGBM
KW - Parkinson's disease
KW - XGBoost
U2 - 10.1016/j.simpa.2023.100504
DO - 10.1016/j.simpa.2023.100504
M3 - Journal article
AN - SCOPUS:85153798902
VL - 16
JO - Software Impacts
JF - Software Impacts
SN - 2665-9638
M1 - 100504
ER -