Early Detection of Parkinson's Disease: Ensemble Learning for Improved Diagnosis
All over the world, 55% of old age people have Parkinson's disease. The patient faces problems in speech and mobility, and it is difficult to get physical treatment and observation to patients. It is necessary to detect the symptoms of Parkinson's earlier automatically, yet traditional dia...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | EPJ Web of Conferences |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2025/13/epjconf_icetsf2025_01005.pdf |
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| _version_ | 1849422763000856576 |
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| author | Raut Komal Balpande Vijaya |
| author_facet | Raut Komal Balpande Vijaya |
| author_sort | Raut Komal |
| collection | DOAJ |
| description | All over the world, 55% of old age people have Parkinson's disease. The patient faces problems in speech and mobility, and it is difficult to get physical treatment and observation to patients. It is necessary to detect the symptoms of Parkinson's earlier automatically, yet traditional diagnostic methods often lack accuracy. This paper proposed several machine learning algorithms such as Decision Tree, Random Forest, Logistic Regression and Support Vector Machine and design an ensemble of these models to detect and classify Parkinson's disease. This study used a standard benchmark Parkinson's dataset. The SMOTE technique addresses the problem of misbalancing the data. The decision tree extracts the relevant features from the dataset. The final result of the ensemble model achieves a 96.62% accuracy score, which is better than other baseline classifiers. This research highlights the potential of these advanced techniques in clinical settings. |
| format | Article |
| id | doaj-art-c178fc6aa7524ecabcc4522ea583b0c0 |
| institution | Kabale University |
| issn | 2100-014X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Web of Conferences |
| spelling | doaj-art-c178fc6aa7524ecabcc4522ea583b0c02025-08-20T03:30:56ZengEDP SciencesEPJ Web of Conferences2100-014X2025-01-013280100510.1051/epjconf/202532801005epjconf_icetsf2025_01005Early Detection of Parkinson's Disease: Ensemble Learning for Improved DiagnosisRaut Komal0Balpande Vijaya1Priyadarshini College of Engineering, CSE DepartmentPriyadarshini College of Engineering, CSE DepartmentAll over the world, 55% of old age people have Parkinson's disease. The patient faces problems in speech and mobility, and it is difficult to get physical treatment and observation to patients. It is necessary to detect the symptoms of Parkinson's earlier automatically, yet traditional diagnostic methods often lack accuracy. This paper proposed several machine learning algorithms such as Decision Tree, Random Forest, Logistic Regression and Support Vector Machine and design an ensemble of these models to detect and classify Parkinson's disease. This study used a standard benchmark Parkinson's dataset. The SMOTE technique addresses the problem of misbalancing the data. The decision tree extracts the relevant features from the dataset. The final result of the ensemble model achieves a 96.62% accuracy score, which is better than other baseline classifiers. This research highlights the potential of these advanced techniques in clinical settings.https://www.epj-conferences.org/articles/epjconf/pdf/2025/13/epjconf_icetsf2025_01005.pdf |
| spellingShingle | Raut Komal Balpande Vijaya Early Detection of Parkinson's Disease: Ensemble Learning for Improved Diagnosis EPJ Web of Conferences |
| title | Early Detection of Parkinson's Disease: Ensemble Learning for Improved Diagnosis |
| title_full | Early Detection of Parkinson's Disease: Ensemble Learning for Improved Diagnosis |
| title_fullStr | Early Detection of Parkinson's Disease: Ensemble Learning for Improved Diagnosis |
| title_full_unstemmed | Early Detection of Parkinson's Disease: Ensemble Learning for Improved Diagnosis |
| title_short | Early Detection of Parkinson's Disease: Ensemble Learning for Improved Diagnosis |
| title_sort | early detection of parkinson s disease ensemble learning for improved diagnosis |
| url | https://www.epj-conferences.org/articles/epjconf/pdf/2025/13/epjconf_icetsf2025_01005.pdf |
| work_keys_str_mv | AT rautkomal earlydetectionofparkinsonsdiseaseensemblelearningforimproveddiagnosis AT balpandevijaya earlydetectionofparkinsonsdiseaseensemblelearningforimproveddiagnosis |