Random Forest-Based Prediction of Parkinson's Disease Progression Using Acoustic, ASR and Intelligibility Features

被引：0

作者：

Zlotnik, Alexander ^{[1
]}

Montero, Juan M. ^{[1
]}

San-Segundo, Ruben ^{[1
]}

Gallardo-Antolin, Ascension ^{[2
]}

机构：

[1] Univ Politecn Madrid, Speech Technol Grp, ETSIT, E-28040 Madrid, Spain

[2] Univ Carlos III Madrid, Dept Signal Theory & Commun, E-28903 Getafe, Spain

来源：

16TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2015), VOLS 1-5 | 2015年

关键词：

random forest; regression; Parkinson's disease; ASR features; intelligibility;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

The Interspeech ComParE 2015 PC Sub-Challenge consists of automatically determining the degree of Parkinson's condition using exclusively the patient's voice. In this paper, we face this problem as a regression task and in order to succeed, we propose the use of an ensemble learning method, Random Forest (RF), in combination with features of different nature: acoustic characteristics, features derived from the output of an Automatic Speech Recognition system (ASR) and non-intrusive intelligibility measures. The system outperforms the baseline results achieving a relative improvement higher than 19% in the development set.

引用

页码：503 / 507

页数：5

共 50 条

[1] Progress prediction of Parkinson's disease based on graph wavelet transform and attention weighted random forest
Xue, Zaifa
Zhang, Tao
Lin, Liqin
EXPERT SYSTEMS WITH APPLICATIONS, 2022, 203
[2] Prediction of Alzheimer?s Using Random Forest with Radiomic Features
Singh, Anuj
Kumar, Raman
Tiwari, Arvind Kumar
COMPUTER SYSTEMS SCIENCE AND ENGINEERING, 2023, 45 (01): : 513 - 530
[3] Predicting Parkinson's Disease Progression with Random Forests
Qian, Shuxin
PROCEEDINGS OF 2024 4TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND INTELLIGENT COMPUTING, BIC 2024, 2024, : 191 - 199
[4] Acoustic characteristics in relation to intelligibility reduction in noise for speakers with Parkinson's disease
Chiu, Yi-Fang
Neel, Amy
Loux, Travis
CLINICAL LINGUISTICS & PHONETICS, 2021, 35 (03) : 222 - 236
[5] Multifidelity aerodynamic flow field prediction using random forest-based machine learning
Nagawkar, Jethro
Leifsson, Leifur
AEROSPACE SCIENCE AND TECHNOLOGY, 2022, 123
[6] Risk factors associated with severe progression of Parkinson's disease: random forest and logistic regression models
Tan, Jie
Huang, E.
Hao, Yang
Wan, Hongping
Zhang, Qian
FRONTIERS IN NEUROLOGY, 2025, 16
[7] RF-Phos: Random Forest-Based Prediction of Phosphorylation Sites
Jones, Ahoi
Ismail, Hamid
Kim, Jung H.
Newman, Robert
Kc, Dukka B.
PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE, 2015, : 135 - 140
[8] Prediction of cell penetrating peptides and their uptake efficiency using random forest-based feature selections
Liu, Peng
Ding, Yijie
Rong, Ying
Chen, Dong
AICHE JOURNAL, 2022, 68 (09)
[9] Improving Landslides Prediction: Meteorological Data Preprocessing Using Random Forest-Based Feature Selection
Guerrero Rodriguez, Byron
Salvador Meneses, Jaime
Garcia-Rodriguez, Jose
16TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING MODELS IN INDUSTRIAL AND ENVIRONMENTAL APPLICATIONS (SOCO 2021), 2022, 1401 : 379 - 387
[10] Random Forest-Based Prediction Models for Assessing Cardiovascular Disease Risk: Integrating Clinical and Genetic Factors
Swarnkar, Suman Kumar
Parthiban, M.
Dixit, Rohit R.
Kalavadekar, Prakash
Patil, Sarika B.
Ambhaikar, Asha
JOURNAL OF ELECTRICAL SYSTEMS, 2024, 20 (03) : 663 - 672

← 1 2 3 4 5 →