Model for Noise-Induced Hearing Loss Using Support Vector Machine
-
2005/09/01
-
Details
-
Personal Author:
-
Description:Contemporary noise standards are based on the assumption that an energy metric such as the equivalent noise level is sufficient for estimating the potential of a noise stimulus to cause noise-induced hearing loss (NIHL). Available data, from laboratory-based experiments indicate that while an energy metric may be necessary, it is not sufficient for the prediction of NIHL. A support vector machine(SVM)NIHL prediction model was constructed, based on a 550-subject (noise-exposed chinchillas) database. Training of the model used data from 367 noise-exposed subjects. The model was tested using the remaining 183 subjects. Input variables for the model included acoustic, audiometric, and biological variables, while output variables were PTS and cell loss. The results show that an energy parameter is not sufficient to predict NIHL, especially in complex noise environments. With the kurtosis and other noise and biological parameters included as additional inputs, the performance of SVM prediction model was significantly improved. The SVM prediction model has the potential to reliably predict noiseinduced hearing loss. [Description provided by NIOSH]
-
Subjects:
-
Keywords:
-
ISSN:0001-4966
-
Document Type:
-
Funding:
-
Genre:
-
Place as Subject:
-
CIO:
-
Topic:
-
Location:
-
Volume:118
-
Issue:3
-
NIOSHTIC Number:nn:20034176
-
Citation:J Acoust Soc Am 2005 Sep; 118(3)(Pt 2):1896
-
Contact Point Address:Wei Qiu, PhD, Auditory Research Laboratory, State University of New York at Plattsburgh, 101 Broad Street, Plattsburgh, New York, 12901
-
Email:qiuw@plattsburgh.edu
-
Federal Fiscal Year:2005
-
NORA Priority Area:
-
Performing Organization:Plattsburgh State University
-
Peer Reviewed:False
-
Part Number:2
-
Start Date:20040901
-
Source Full Name:Journal of the Acoustical Society of America
-
End Date:20060831
-
Collection(s):
-
Main Document Checksum:urn:sha-512:40207297e9b9c1528714ba3a7ec07ff344f1d5c43c591e581858e18d2f8ef6667df80ebecca165143289184826a2c59cda4ca7bf3705291daebc25950e0d87a9
-
Download URL:
-
File Type:
ON THIS PAGE
CDC STACKS serves as an archival repository of CDC-published products including
scientific findings,
journal articles, guidelines, recommendations, or other public health information authored or
co-authored by CDC or funded partners.
As a repository, CDC STACKS retains documents in their original published format to ensure public access to scientific information.
As a repository, CDC STACKS retains documents in their original published format to ensure public access to scientific information.
You May Also Like