Healthcare Big Data Voice Pathology Assessment Framework
The fast-growing healthcare big data plays an important role in healthcare service providing. Healthcare big data comprise data from different structured, semi-structured, and unstructured sources. These data sources vary in terms of heterogeneity, volume, variety, velocity, and value that tradition...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2016-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/7738441/ |
id |
doaj-b09f81dd898043cb8403e4024e89a69c |
---|---|
record_format |
Article |
spelling |
doaj-b09f81dd898043cb8403e4024e89a69c2021-03-29T19:47:13ZengIEEEIEEE Access2169-35362016-01-0147806781510.1109/ACCESS.2016.26263167738441Healthcare Big Data Voice Pathology Assessment FrameworkM. Shamim Hossain0https://orcid.org/0000-0001-5906-9422Ghulam Muhammad1Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaThe fast-growing healthcare big data plays an important role in healthcare service providing. Healthcare big data comprise data from different structured, semi-structured, and unstructured sources. These data sources vary in terms of heterogeneity, volume, variety, velocity, and value that traditional frameworks, algorithms, tools, and techniques are not fully capable of handling. Therefore, a framework is required that facilitates collection, extraction, storage, classification, processing, and modeling of this vast heterogeneous volume of data. This paper proposes a healthcare big data framework using voice pathology assessment (VPA) as a case study. In the proposed VPA system, two robust features, MPEG-7 low-level audio and the interlaced derivative pattern, are used for processing the voice or speech signals. The machine learning algorithms in the form of a support vector machine, an extreme learning machine, and a Gaussian mixture model are used as the classifier. In the experiments, the proposed VPA system shows its efficiency in terms of accuracy and time requirement.https://ieeexplore.ieee.org/document/7738441/Healthcare big datavoice pathologyclassificationfeature extraction |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
M. Shamim Hossain Ghulam Muhammad |
spellingShingle |
M. Shamim Hossain Ghulam Muhammad Healthcare Big Data Voice Pathology Assessment Framework IEEE Access Healthcare big data voice pathology classification feature extraction |
author_facet |
M. Shamim Hossain Ghulam Muhammad |
author_sort |
M. Shamim Hossain |
title |
Healthcare Big Data Voice Pathology Assessment Framework |
title_short |
Healthcare Big Data Voice Pathology Assessment Framework |
title_full |
Healthcare Big Data Voice Pathology Assessment Framework |
title_fullStr |
Healthcare Big Data Voice Pathology Assessment Framework |
title_full_unstemmed |
Healthcare Big Data Voice Pathology Assessment Framework |
title_sort |
healthcare big data voice pathology assessment framework |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2016-01-01 |
description |
The fast-growing healthcare big data plays an important role in healthcare service providing. Healthcare big data comprise data from different structured, semi-structured, and unstructured sources. These data sources vary in terms of heterogeneity, volume, variety, velocity, and value that traditional frameworks, algorithms, tools, and techniques are not fully capable of handling. Therefore, a framework is required that facilitates collection, extraction, storage, classification, processing, and modeling of this vast heterogeneous volume of data. This paper proposes a healthcare big data framework using voice pathology assessment (VPA) as a case study. In the proposed VPA system, two robust features, MPEG-7 low-level audio and the interlaced derivative pattern, are used for processing the voice or speech signals. The machine learning algorithms in the form of a support vector machine, an extreme learning machine, and a Gaussian mixture model are used as the classifier. In the experiments, the proposed VPA system shows its efficiency in terms of accuracy and time requirement. |
topic |
Healthcare big data voice pathology classification feature extraction |
url |
https://ieeexplore.ieee.org/document/7738441/ |
work_keys_str_mv |
AT mshamimhossain healthcarebigdatavoicepathologyassessmentframework AT ghulammuhammad healthcarebigdatavoicepathologyassessmentframework |
_version_ |
1724195755830280192 |