Improving detection of Parkinson’s disease with acoustic feature optimization using particle swarm optimization and machine learning
Parkinson’s disease (PD), characterized by motor impairments and tremors, also presents early-stage vocal abnormalities that hold diagnostic potential. Leveraging voice analysis and classification techniques, numerous studies explore the feasibility of early PD detection through automated systems. W...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/adadc3 |
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| _version_ | 1832540933874253824 |
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| author | Elmoundher Hadjaidji Mohamed Cherif Amara Korba Khaled Khelil |
| author_facet | Elmoundher Hadjaidji Mohamed Cherif Amara Korba Khaled Khelil |
| author_sort | Elmoundher Hadjaidji |
| collection | DOAJ |
| description | Parkinson’s disease (PD), characterized by motor impairments and tremors, also presents early-stage vocal abnormalities that hold diagnostic potential. Leveraging voice analysis and classification techniques, numerous studies explore the feasibility of early PD detection through automated systems. While several databases offer acoustic features for this purpose, their effectiveness largely depends on the classification methodology employed. This study aims to refine PD detection systems by introducing customized weighting to acoustic features, adjusting their significance based on their correlation with the disease and the classification algorithm utilized. The particle swarm optimization algorithm is employed for this purpose, with the Oxford PD dataset serving as the source data for training and validation. Performance evaluation encompasses four classification algorithms: support vector machine, Gradient Boosting (GB), k-nearest neighbors (KNN), and Naïve Bayes. A 5-fold cross-validation technique was adopted to evaluate the effectiveness of our method for PD detection. The results show that our approach significantly improves performance regardless of the classifier used, demonstrating its generalization capability. The KNN classifier surpasses state-of-the-art results, achieving an accuracy of 97.44% and a sensitivity of 98.02%. |
| format | Article |
| id | doaj-art-a664a1bfd68a455a99c63d6fa4bbcd02 |
| institution | Kabale University |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-a664a1bfd68a455a99c63d6fa4bbcd022025-02-04T11:48:59ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016101502610.1088/2632-2153/adadc3Improving detection of Parkinson’s disease with acoustic feature optimization using particle swarm optimization and machine learningElmoundher Hadjaidji0https://orcid.org/0000-0002-0336-2491Mohamed Cherif Amara Korba1https://orcid.org/0000-0003-3888-8765Khaled Khelil2https://orcid.org/0000-0001-7237-2725Faculty of Science and Technology, LEER Lab, Mohamed Cherif Messaadia University , Souk Ahras 41000, AlgeriaFaculty of Science and Technology, LEER Lab, Mohamed Cherif Messaadia University , Souk Ahras 41000, AlgeriaFaculty of Science and Technology, LEER Lab, Mohamed Cherif Messaadia University , Souk Ahras 41000, AlgeriaParkinson’s disease (PD), characterized by motor impairments and tremors, also presents early-stage vocal abnormalities that hold diagnostic potential. Leveraging voice analysis and classification techniques, numerous studies explore the feasibility of early PD detection through automated systems. While several databases offer acoustic features for this purpose, their effectiveness largely depends on the classification methodology employed. This study aims to refine PD detection systems by introducing customized weighting to acoustic features, adjusting their significance based on their correlation with the disease and the classification algorithm utilized. The particle swarm optimization algorithm is employed for this purpose, with the Oxford PD dataset serving as the source data for training and validation. Performance evaluation encompasses four classification algorithms: support vector machine, Gradient Boosting (GB), k-nearest neighbors (KNN), and Naïve Bayes. A 5-fold cross-validation technique was adopted to evaluate the effectiveness of our method for PD detection. The results show that our approach significantly improves performance regardless of the classifier used, demonstrating its generalization capability. The KNN classifier surpasses state-of-the-art results, achieving an accuracy of 97.44% and a sensitivity of 98.02%.https://doi.org/10.1088/2632-2153/adadc3Parkinson’s disease detectionvoice disordersPSOacoustic feature weighting |
| spellingShingle | Elmoundher Hadjaidji Mohamed Cherif Amara Korba Khaled Khelil Improving detection of Parkinson’s disease with acoustic feature optimization using particle swarm optimization and machine learning Machine Learning: Science and Technology Parkinson’s disease detection voice disorders PSO acoustic feature weighting |
| title | Improving detection of Parkinson’s disease with acoustic feature optimization using particle swarm optimization and machine learning |
| title_full | Improving detection of Parkinson’s disease with acoustic feature optimization using particle swarm optimization and machine learning |
| title_fullStr | Improving detection of Parkinson’s disease with acoustic feature optimization using particle swarm optimization and machine learning |
| title_full_unstemmed | Improving detection of Parkinson’s disease with acoustic feature optimization using particle swarm optimization and machine learning |
| title_short | Improving detection of Parkinson’s disease with acoustic feature optimization using particle swarm optimization and machine learning |
| title_sort | improving detection of parkinson s disease with acoustic feature optimization using particle swarm optimization and machine learning |
| topic | Parkinson’s disease detection voice disorders PSO acoustic feature weighting |
| url | https://doi.org/10.1088/2632-2153/adadc3 |
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