A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests
Recently, speech pattern analysis applications in building predictive telediagnosis and telemonitoring models for diagnosing Parkinson’s disease (PD) have attracted many researchers. For this purpose, several datasets of voice samples exist; the UCI dataset named “Parkinson Speech Dataset with Multi...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | International Journal of Telemedicine and Applications |
| Online Access: | http://dx.doi.org/10.1155/2016/6837498 |
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| author | Mahnaz Behroozi Ashkan Sami |
| author_facet | Mahnaz Behroozi Ashkan Sami |
| author_sort | Mahnaz Behroozi |
| collection | DOAJ |
| description | Recently, speech pattern analysis applications in building predictive telediagnosis and telemonitoring models for diagnosing Parkinson’s disease (PD) have attracted many researchers. For this purpose, several datasets of voice samples exist; the UCI dataset named “Parkinson Speech Dataset with Multiple Types of Sound Recordings” has a variety of vocal tests, which include sustained vowels, words, numbers, and short sentences compiled from a set of speaking exercises for healthy and people with Parkinson’s disease (PWP). Some researchers claim that summarizing the multiple recordings of each subject with the central tendency and dispersion metrics is an efficient strategy in building a predictive model for PD. However, they have overlooked the point that a PD patient may show more difficulty in pronouncing certain terms than the other terms. Thus, summarizing the vocal tests may lead into loss of valuable information. In order to address this issue, the classification setting must take what has been said into account. As a solution, we introduced a new framework that applies an independent classifier for each vocal test. The final classification result would be a majority vote from all of the classifiers. When our methodology comes with filter-based feature selection, it enhances classification accuracy up to 15%. |
| format | Article |
| id | doaj-art-5ab7e42ee17f49b3aaaf178b2d7cc2fb |
| institution | Kabale University |
| issn | 1687-6415 1687-6423 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Telemedicine and Applications |
| spelling | doaj-art-5ab7e42ee17f49b3aaaf178b2d7cc2fb2025-02-03T01:04:45ZengWileyInternational Journal of Telemedicine and Applications1687-64151687-64232016-01-01201610.1155/2016/68374986837498A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal TestsMahnaz Behroozi0Ashkan Sami1Department of CSE and IT, School of Electrical Engineering and Computer Science, Shiraz University, Shiraz 71348-51154, IranDepartment of CSE and IT, School of Electrical Engineering and Computer Science, Shiraz University, Shiraz 71348-51154, IranRecently, speech pattern analysis applications in building predictive telediagnosis and telemonitoring models for diagnosing Parkinson’s disease (PD) have attracted many researchers. For this purpose, several datasets of voice samples exist; the UCI dataset named “Parkinson Speech Dataset with Multiple Types of Sound Recordings” has a variety of vocal tests, which include sustained vowels, words, numbers, and short sentences compiled from a set of speaking exercises for healthy and people with Parkinson’s disease (PWP). Some researchers claim that summarizing the multiple recordings of each subject with the central tendency and dispersion metrics is an efficient strategy in building a predictive model for PD. However, they have overlooked the point that a PD patient may show more difficulty in pronouncing certain terms than the other terms. Thus, summarizing the vocal tests may lead into loss of valuable information. In order to address this issue, the classification setting must take what has been said into account. As a solution, we introduced a new framework that applies an independent classifier for each vocal test. The final classification result would be a majority vote from all of the classifiers. When our methodology comes with filter-based feature selection, it enhances classification accuracy up to 15%.http://dx.doi.org/10.1155/2016/6837498 |
| spellingShingle | Mahnaz Behroozi Ashkan Sami A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests International Journal of Telemedicine and Applications |
| title | A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests |
| title_full | A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests |
| title_fullStr | A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests |
| title_full_unstemmed | A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests |
| title_short | A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests |
| title_sort | multiple classifier framework for parkinson s disease detection based on various vocal tests |
| url | http://dx.doi.org/10.1155/2016/6837498 |
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