Random Forest Algorithm Based on Speech for Early Identification of Parkinson’s Disease

Abstract

To investigate the effectiveness of identifying patients with Parkinson’s disease (PD) from speech signals, various acoustic parameters including prosodic and segmental features are extracted from speech and then the random forest classification (RF) algorithm based on these acoustic parameters is applied to diagnose early-stage PD patients. To validate the proposed method of RF algorithm in early-stage PD identification, this study compares the accuracy rate of RF with that of neurologists’ judgments based on auditory test outcomes, and the results clearly show the superiority of the proposed method over its rival. Random forest algorithm based on speech can improve the accuracy of patients’ identification, which provides an efficient auxiliary method in the early diagnosis of PD patients.

Deducing Leading Factors of Spatial Distribution of Carbon Reserves in Nanjing Metropolitan Area Based on Random Forest Model

“…The random forest algorithm is a combination classification algorithm proposed by Breiman [ 28 ], and it is a supervised learning algorithm that integrates multiple decision trees [ 28 , 35 ]. It is often used for classification and regression in machine learning.…”

“…Models established for NMA, UCA, UPA, and UEA are designated as model I, III, IV, and IV, respectively. According to the established RF model [49][50][51][52][53][54][55][56][57][58], the R 2 of each level can be calculated. e numb of ntree is set as 100 as a parameter input.…”

“…Compared with the other methods, the random forest is suitable for processing high-dimensional data, is not easy to generate overfitting, and performs well when processing the data [ 31 , 33 , 34 ]. At present, RF model has been widely used in medicine [ 35 , 36 ], economics [ 37 , 38 ], remote sense [ 39 , 40 ], and some other fields, but it has not been widely used in the research of carbon reserves.…”

An Intrusion Detection Method Based on Fully Connected Recurrent Neural Network

“…Using the same dataset NSL-KDD, the detection accuracy of seven traditional machine learning algorithms [20][21][22][23][24][25][26][27][28][29][30][31][32][33] such as decision tree, Naive Bayes, Naive Bayes tree, random tree, random forest, support vector machine, multilayer perceptron, and the detection accuracy of the FCRNN-IDS model in the case of 2-class (normal, abnormal) and 5-class (normal, probe, Dod, R2L and U2R) were studied and compared.…”

Retracted: Random Forest Algorithm Based on Speech for Early Identification of Parkinson’s Disease

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”