Predicting Parkinson’s Disease Using Machine Learning with Voice Parameters and Handwriting Images
Most studies have failed to focus on geriatric diseases in the present era of quick advancement in medical science. Diseases like Parkinson’s display their symptoms at a later stage and make a complete recovery almost doubtful. Parkinson’s disease is a neurodegenerative disorder that affects movemen...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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INTI International University
2024
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| Online Access: | http://eprints.intimal.edu.my/1948/ http://eprints.intimal.edu.my/1948/1/jods2024_19.pdf |
| _version_ | 1848766879617777664 |
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| author | Pruthvi, H.C. UshaSree, R Harprith, Kaur |
| author_facet | Pruthvi, H.C. UshaSree, R Harprith, Kaur |
| author_sort | Pruthvi, H.C. |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | Most studies have failed to focus on geriatric diseases in the present era of quick advancement in medical science. Diseases like Parkinson’s display their symptoms at a later stage and make a complete recovery almost doubtful. Parkinson’s disease is a neurodegenerative disorder that affects movement and motor control systems. It is named after Dr. James Parkinson, the first person affected by this disease. Parkinson’s slowly worsens over time, leading to a variety of syndromes that can impact a person’s daily life activities. More than 95% of Parkinson’s Disease (PD) patients stated that they have exhibited voice impairment and micrographic disability. This model takes advantage of both advanced machine learning algorithms and modern image processing techniques, resulting in effective and efficient prediction PD. To further enhance the accuracy of the model, we have incorporated additional algorithms such as Random Forest and K-nearest Neighbour. Random forest classifier has a detection accuracy of 92%and sensitivity of 0.95%. The performance has been assessed with a reliable dataset from the University of California Irvine Machine Learning repository for voice parameters and a dataset from Kaggle for Handwriting images which includes wavy images and spiral images. Our proposed model has achieved the highest accuracy of 95% which outperformed the previous model or experiment on the same dataset. |
| first_indexed | 2025-11-14T11:58:10Z |
| format | Article |
| id | intimal-1948 |
| institution | INTI International University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:58:10Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-19482024-07-24T06:10:49Z http://eprints.intimal.edu.my/1948/ Predicting Parkinson’s Disease Using Machine Learning with Voice Parameters and Handwriting Images Pruthvi, H.C. UshaSree, R Harprith, Kaur QA75 Electronic computers. Computer science RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry T Technology (General) Most studies have failed to focus on geriatric diseases in the present era of quick advancement in medical science. Diseases like Parkinson’s display their symptoms at a later stage and make a complete recovery almost doubtful. Parkinson’s disease is a neurodegenerative disorder that affects movement and motor control systems. It is named after Dr. James Parkinson, the first person affected by this disease. Parkinson’s slowly worsens over time, leading to a variety of syndromes that can impact a person’s daily life activities. More than 95% of Parkinson’s Disease (PD) patients stated that they have exhibited voice impairment and micrographic disability. This model takes advantage of both advanced machine learning algorithms and modern image processing techniques, resulting in effective and efficient prediction PD. To further enhance the accuracy of the model, we have incorporated additional algorithms such as Random Forest and K-nearest Neighbour. Random forest classifier has a detection accuracy of 92%and sensitivity of 0.95%. The performance has been assessed with a reliable dataset from the University of California Irvine Machine Learning repository for voice parameters and a dataset from Kaggle for Handwriting images which includes wavy images and spiral images. Our proposed model has achieved the highest accuracy of 95% which outperformed the previous model or experiment on the same dataset. INTI International University 2024-07 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/1948/1/jods2024_19.pdf Pruthvi, H.C. and UshaSree, R and Harprith, Kaur (2024) Predicting Parkinson’s Disease Using Machine Learning with Voice Parameters and Handwriting Images. Journal of Data Science, 2024 (19). pp. 1-7. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | QA75 Electronic computers. Computer science RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry T Technology (General) Pruthvi, H.C. UshaSree, R Harprith, Kaur Predicting Parkinson’s Disease Using Machine Learning with Voice Parameters and Handwriting Images |
| title | Predicting Parkinson’s Disease Using Machine Learning with Voice Parameters and Handwriting Images |
| title_full | Predicting Parkinson’s Disease Using Machine Learning with Voice Parameters and Handwriting Images |
| title_fullStr | Predicting Parkinson’s Disease Using Machine Learning with Voice Parameters and Handwriting Images |
| title_full_unstemmed | Predicting Parkinson’s Disease Using Machine Learning with Voice Parameters and Handwriting Images |
| title_short | Predicting Parkinson’s Disease Using Machine Learning with Voice Parameters and Handwriting Images |
| title_sort | predicting parkinson’s disease using machine learning with voice parameters and handwriting images |
| topic | QA75 Electronic computers. Computer science RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry T Technology (General) |
| url | http://eprints.intimal.edu.my/1948/ http://eprints.intimal.edu.my/1948/ http://eprints.intimal.edu.my/1948/1/jods2024_19.pdf |