An Optimized Hybrid PSO-ELM for Parkinson’s Disease Diagnosis
Parkinson’s disease (PD) is a neurodegenerative disease that gradually causes movement impairment and various symptoms. It is difficult to precisely diagnose PD, especially in its early stages, because the signs and symptoms can resemble those of other medical conditions or normal age-rel...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10807212/ |
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| author | Gunakala Archana Afzal Hussain Shahid |
| author_facet | Gunakala Archana Afzal Hussain Shahid |
| author_sort | Gunakala Archana |
| collection | DOAJ |
| description | Parkinson’s disease (PD) is a neurodegenerative disease that gradually causes movement impairment and various symptoms. It is difficult to precisely diagnose PD, especially in its early stages, because the signs and symptoms can resemble those of other medical conditions or normal age-related changes. This paper proposes a hybrid model combining Particle Swarm Optimization with Extreme Learning Machine (PSO-ELM) for PD diagnosis. This paper employs three feature ranking algorithms, namely ReliefF, minimum Redundancy Maximum relevance (mRMR), and Fisher, on six publicly available PD datasets. Various top-ranked feature subsets are created to identify the most discriminative features and enhance the performance of the proposed hybrid PSO-ELM model for all datasets. Furthermore, the efficiency of the proposed model is compared with basic models, namely ELM, SVM, RF, and the previous works. The results show that the proposed PSO-ELM model achieved the highest average accuracy, recall, precision, and F1-score of 100% each over the 3-fold cross-validation with a minimum number of features for all the six datasets. Therefore, the PSO-ELM model may be used as a highly accurate and efficient tool for PD diagnosis. |
| format | Article |
| id | doaj-art-1077bc3a8f6a4be98d98607199bd9136 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1077bc3a8f6a4be98d98607199bd91362025-01-07T00:02:21ZengIEEEIEEE Access2169-35362025-01-01132546257010.1109/ACCESS.2024.352048210807212An Optimized Hybrid PSO-ELM for Parkinson’s Disease DiagnosisGunakala Archana0https://orcid.org/0000-0002-3375-1893Afzal Hussain Shahid1https://orcid.org/0009-0001-9815-108XSchool of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, IndiaSchool of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, IndiaParkinson’s disease (PD) is a neurodegenerative disease that gradually causes movement impairment and various symptoms. It is difficult to precisely diagnose PD, especially in its early stages, because the signs and symptoms can resemble those of other medical conditions or normal age-related changes. This paper proposes a hybrid model combining Particle Swarm Optimization with Extreme Learning Machine (PSO-ELM) for PD diagnosis. This paper employs three feature ranking algorithms, namely ReliefF, minimum Redundancy Maximum relevance (mRMR), and Fisher, on six publicly available PD datasets. Various top-ranked feature subsets are created to identify the most discriminative features and enhance the performance of the proposed hybrid PSO-ELM model for all datasets. Furthermore, the efficiency of the proposed model is compared with basic models, namely ELM, SVM, RF, and the previous works. The results show that the proposed PSO-ELM model achieved the highest average accuracy, recall, precision, and F1-score of 100% each over the 3-fold cross-validation with a minimum number of features for all the six datasets. Therefore, the PSO-ELM model may be used as a highly accurate and efficient tool for PD diagnosis.https://ieeexplore.ieee.org/document/10807212/Classificationextreme learning machinefeature rankingParkinson’s diseaseparticle swarm optimization |
| spellingShingle | Gunakala Archana Afzal Hussain Shahid An Optimized Hybrid PSO-ELM for Parkinson’s Disease Diagnosis IEEE Access Classification extreme learning machine feature ranking Parkinson’s disease particle swarm optimization |
| title | An Optimized Hybrid PSO-ELM for Parkinson’s Disease Diagnosis |
| title_full | An Optimized Hybrid PSO-ELM for Parkinson’s Disease Diagnosis |
| title_fullStr | An Optimized Hybrid PSO-ELM for Parkinson’s Disease Diagnosis |
| title_full_unstemmed | An Optimized Hybrid PSO-ELM for Parkinson’s Disease Diagnosis |
| title_short | An Optimized Hybrid PSO-ELM for Parkinson’s Disease Diagnosis |
| title_sort | optimized hybrid pso elm for parkinson x2019 s disease diagnosis |
| topic | Classification extreme learning machine feature ranking Parkinson’s disease particle swarm optimization |
| url | https://ieeexplore.ieee.org/document/10807212/ |
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