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Title: :  Parkinson’s Disease Prediction Using Machine Learning.
PaperId: :  23585
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 3 2024
DUI:    16.0415/IJARIIE-23585
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Sumaathi.PDON BOSCO INSTITUTE OF TECHNOLOGY
Syed Ameenuddin AmaanDON BOSCO INSTITUTE OF TECHNOLOGY
Vishal.A.SDON BOSCO INSTITUTE OF TECHNOLOGY
R.Yashodara Assistant ProfessorDON BOSCO INSTITUTE OF TECHNOLOGY
Assistant Prof. Divyashree.KDON BOSCO INSTITUTE OF TECHNOLOGY

Abstract

Information Science And Engineering
Parkinson’s disease, Early detection, Premotor features, Prediction, Features importance.
Parkinson’s disease (PD) is a neurodegenerative disorder that affects millions of people worldwide, causing tremors, stiffness, and difficulty with movement. Early diagnosis and intervention are crucial for managing PD effectively and improving patients' quality of life. In recent years, machine learning (ML) algorithms have shown promise in assisting with the early detection and prediction of PD based on various clinical and biomarker data. This study aims to explore the effectiveness of ML techniques in predicting Parkinson’s disease using relevant features extracted from patient data. A comprehensive dataset comprising demographic information, clinical assessments, and possibly biomarkers is collected from individuals with and without PD. Various ML algorithms, including but not limited to logistic regression, support vector machines, decision trees, random forests, and neural networks, are employed to build predictive models. Feature selection techniques and cross-validation are applied to optimize model performance and generalizability. The performance of each model is evaluated using metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). Additionally, feature importance analysis is conducted to identify the most discriminative features for PD prediction. The proposed ML models are compared with traditional diagnostic methods to assess their potential clinical utility in early PD detection. The results of this study will contribute to the growing body of research on leveraging ML for the prediction and early diagnosis of Parkinson’s disease. By developing accurate and efficient prediction models, healthcare professionals can potentially identify individuals at risk of developing PD at an earlier stage, enabling timely interventions and personalized treatment strategies. Moreover, the insights gained from this research may pave the way for the development of user-friendly and cost-effective diagnostic tools for Parkinson’s disease prediction in clinical practice.

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IJARIIE Sumaathi.P, Syed Ameenuddin Amaan, Vishal.A.S, R.Yashodara Assistant Professor, and Assistant Prof. Divyashree.K. "Parkinson’s Disease Prediction Using Machine Learning." International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 3 2024 Page 31-35
MLA Sumaathi.P, Syed Ameenuddin Amaan, Vishal.A.S, R.Yashodara Assistant Professor, and Assistant Prof. Divyashree.K. "Parkinson’s Disease Prediction Using Machine Learning.." International Journal Of Advance Research And Innovative Ideas In Education 10.3(2024) : 31-35.
APA Sumaathi.P, Syed Ameenuddin Amaan, Vishal.A.S, R.Yashodara Assistant Professor, & Assistant Prof. Divyashree.K. (2024). Parkinson’s Disease Prediction Using Machine Learning.. International Journal Of Advance Research And Innovative Ideas In Education, 10(3), 31-35.
Chicago Sumaathi.P, Syed Ameenuddin Amaan, Vishal.A.S, R.Yashodara Assistant Professor, and Assistant Prof. Divyashree.K. "Parkinson’s Disease Prediction Using Machine Learning.." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 3 (2024) : 31-35.
Oxford Sumaathi.P, Syed Ameenuddin Amaan, Vishal.A.S, R.Yashodara Assistant Professor, and Assistant Prof. Divyashree.K. 'Parkinson’s Disease Prediction Using Machine Learning.', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 3, 2024, p. 31-35. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Parkinson’s_Disease_Prediction_Using_Machine_Learning__ijariie23585.pdf (Accessed : 04 June 2025).
Harvard Sumaathi.P, Syed Ameenuddin Amaan, Vishal.A.S, R.Yashodara Assistant Professor, and Assistant Prof. Divyashree.K. (2024) 'Parkinson’s Disease Prediction Using Machine Learning.', International Journal Of Advance Research And Innovative Ideas In Education, 10(3), pp. 31-35IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Parkinson’s_Disease_Prediction_Using_Machine_Learning__ijariie23585.pdf (Accessed : 04 June 2025)
IEEE Sumaathi.P, Syed Ameenuddin Amaan, Vishal.A.S, R.Yashodara Assistant Professor, and Assistant Prof. Divyashree.K, "Parkinson’s Disease Prediction Using Machine Learning.," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 3, pp. 31-35, May-Jun 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/Parkinson’s_Disease_Prediction_Using_Machine_Learning__ijariie23585.pdf [Accessed : 04 June 2025].
Turabian Sumaathi.P, Syed Ameenuddin Amaan, Vishal.A.S, R.Yashodara Assistant Professor, and Assistant Prof. Divyashree.K. "Parkinson’s Disease Prediction Using Machine Learning.." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 3 (04 June 2025).
Vancouver Sumaathi.P, Syed Ameenuddin Amaan, Vishal.A.S, R.Yashodara Assistant Professor, and Assistant Prof. Divyashree.K. Parkinson’s Disease Prediction Using Machine Learning.. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : 04 June 2025]; 10(3) : 31-35. Available from: https://ijariie.com/AdminUploadPdf/Parkinson’s_Disease_Prediction_Using_Machine_Learning__ijariie23585.pdf
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