Predicting Parkinson’s Disease Using Machine Learning Model
This research work discusses the steps involved in developing a machine learning program for the early detection of Parkinson's disease (PD) using a variety of clinical and behavioral data. By utilizing highlights extracted from persistent data, including engine and non-motor side effects, t...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2024
|
| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2081/ http://eprints.intimal.edu.my/2081/2/622 http://eprints.intimal.edu.my/2081/3/joit2024_36b.pdf |
| _version_ | 1848766914862514176 |
|---|---|
| author | Sanjay Aswath, K.S.M Chitra, K. |
| author_facet | Sanjay Aswath, K.S.M Chitra, K. |
| author_sort | Sanjay Aswath, K.S.M |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | This research work discusses the steps involved in developing a machine learning program
for the early detection of Parkinson's disease (PD) using a variety of clinical and behavioral
data. By utilizing highlights extracted from persistent data, including engine and non-motor
side effects, the demonstration employs administered learning procedures to identify
patterns indicative of Parkinson's disease (PD). We assess the performance of various
calculations, including back vector machines and neural systems, to determine the most
effective method for accurate forecasts. The results demonstrate the model's potential to
enhance early diagnosis and personalized treatment strategies for Parkinson's infection.
Parkinson's disease (PD) is a dynamic neurodegenerative disorder characterized by engine
side effects such as tremors, inflexibility, and bradykinesia, as well as non-motor side effects
including cognitive disability and autonomic brokenness. Early and precise diagnosis is
essential for effective management and treatment of the infection. In later years, machine
learning (ML) has risen as an effective device in the field of therapeutic diagnostics,
advertising potential changes in the early location and observation of Parkinson's malady. |
| first_indexed | 2025-11-14T11:58:43Z |
| format | Article |
| id | intimal-2081 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:58:43Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-20812025-07-12T03:03:54Z http://eprints.intimal.edu.my/2081/ Predicting Parkinson’s Disease Using Machine Learning Model Sanjay Aswath, K.S.M Chitra, K. QA75 Electronic computers. Computer science QA76 Computer software R Medicine (General) T Technology (General) This research work discusses the steps involved in developing a machine learning program for the early detection of Parkinson's disease (PD) using a variety of clinical and behavioral data. By utilizing highlights extracted from persistent data, including engine and non-motor side effects, the demonstration employs administered learning procedures to identify patterns indicative of Parkinson's disease (PD). We assess the performance of various calculations, including back vector machines and neural systems, to determine the most effective method for accurate forecasts. The results demonstrate the model's potential to enhance early diagnosis and personalized treatment strategies for Parkinson's infection. Parkinson's disease (PD) is a dynamic neurodegenerative disorder characterized by engine side effects such as tremors, inflexibility, and bradykinesia, as well as non-motor side effects including cognitive disability and autonomic brokenness. Early and precise diagnosis is essential for effective management and treatment of the infection. In later years, machine learning (ML) has risen as an effective device in the field of therapeutic diagnostics, advertising potential changes in the early location and observation of Parkinson's malady. INTI International University 2024-12 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2081/2/622 text en http://eprints.intimal.edu.my/2081/3/joit2024_36b.pdf Sanjay Aswath, K.S.M and Chitra, K. (2024) Predicting Parkinson’s Disease Using Machine Learning Model. Journal of Innovation and Technology, 2024 (36). pp. 1-7. ISSN 2805-5179 http://ipublishing.intimal.edu.my/joint.html |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software R Medicine (General) T Technology (General) Sanjay Aswath, K.S.M Chitra, K. Predicting Parkinson’s Disease Using Machine Learning Model |
| title | Predicting Parkinson’s Disease Using Machine Learning Model |
| title_full | Predicting Parkinson’s Disease Using Machine Learning Model |
| title_fullStr | Predicting Parkinson’s Disease Using Machine Learning Model |
| title_full_unstemmed | Predicting Parkinson’s Disease Using Machine Learning Model |
| title_short | Predicting Parkinson’s Disease Using Machine Learning Model |
| title_sort | predicting parkinson’s disease using machine learning model |
| topic | QA75 Electronic computers. Computer science QA76 Computer software R Medicine (General) T Technology (General) |
| url | http://eprints.intimal.edu.my/2081/ http://eprints.intimal.edu.my/2081/ http://eprints.intimal.edu.my/2081/2/622 http://eprints.intimal.edu.my/2081/3/joit2024_36b.pdf |