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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Parkinson’s Diseases Prediction and Comparison of Machine Learning Algorithms |
| Authors Name: | Ujjwal Chandrakant Chaudhari , Mansi Ingle , Anchal Barbate , Palak Bhagat |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_192147
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| Published Paper Id: | IJNRD2304574 |
| Published In: | Volume 8 Issue 4, April-2023 |
| DOI: | |
| Abstract: | Parkinson's disease (PD) is a neurological disorder that affects a significant number of people worldwide. Timely and accurate prediction of PD can help in early intervention and treatment, improving patient outcomes. While there is currently no known cure for the disease, early detection and treatment can reduce the cost of the disease and save lives. However, proper and timely detection of Parkinson's disease is challenging in underdeveloped countries due to limited resources and awareness. Additionally, symptoms vary among patients and may not all become apparent at the same stage of the disease.In this study, we investigate the application of machine learning techniques to predict PD using clinical data, with a focus on voice degradation as a symptom. We utilized various state-of-the-art machine learning algorithms, such as K-Nearest Neighbours (KNN), Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest Classifier, and XGBoost Classifier, to determine which algorithm is best suited for PD prediction. The performance evaluation parameters, including accuracy, precision, recall, F1 score, and Precision-Recall curve (PR curve), were used to compare the algorithms. We obtained the dataset for the study from the Oxford UCI Machine repository.Our study found that all four machine learning algorithms achieved high accuracy in predicting PD, with XGBoost achieving the highest accuracy of 96.61%, followed by Random Forest with 94.91%, KNN with 91.52%, and Decision Tree with 86.44%. Our study highlights the potential of machine learning techniques in accurately predicting PD using clinical data. The findings suggest that XGBoost, Random Forest, and KNN are effective tools for early PD prediction, providing valuable insights for clinical decision-making and personalized treatment planning. |
| Keywords: | Parkinson's disease, machine learning, prediction, XGBoost, Random Forest, KNN |
| Cite Article: | "Parkinson’s Diseases Prediction and Comparison of Machine Learning Algorithms", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 4, page no.f632-f637, April-2023, Available :http://www.ijnrd.org/papers/IJNRD2304574.pdf |
| Downloads: | 000118761 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2304574 Registration ID: 192147 Published In: Volume 8 Issue 4, April-2023 DOI (Digital Object Identifier): Page No: f632-f637 Country: katol, maharashtra, India Research Area: Information Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2304574 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2304574 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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