Research Article
Prognoza: Parkinson’s Disease Prediction Using Classification Algorithms
@ARTICLE{10.4108/eetpht.9.3933, author={Mithun Shivakoti and Sai Charan Medaramatla and Narsaiah Shivakoti}, title={Prognoza: Parkinson’s Disease Prediction Using Classification Algorithms}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={9}, number={1}, publisher={EAI}, journal_a={PHAT}, year={2023}, month={9}, keywords={Parkinson's disease, Classification, Machine Learning, CATBoost, Random Oversampling}, doi={10.4108/eetpht.9.3933} }
- Mithun Shivakoti
Sai Charan Medaramatla
Narsaiah Shivakoti
Year: 2023
Prognoza: Parkinson’s Disease Prediction Using Classification Algorithms
PHAT
EAI
DOI: 10.4108/eetpht.9.3933
Abstract
Parkinson's Disease (PD) is a persistent neurological condition that has a global impact on a significant number of individuals. The timely detection of PD is imperative for the efficacious treatment and control of the condition. Machine learning (ML) methods have demonstrated significant potential in forecasting Parkinson's disease (PD) based on diverse data sources in recent times. The present research paper outlines a study that employs machine learning [ML]techniques to predict Parkinson's disease. A dataset comprising clinical and demographic characteristics of both patients diagnosed with PD and healthy individuals was taken from Kaggle. The aforementioned dataset was utilized to train and assess multiple machine learning models. The experimental findings indicate that the CatBoost model exhibited superior performance compared to the other models, achieving an accuracy rate of 95.1% and a root mean squared error of of 0.34.In summary, our research showcases the capabilities of machine learning methodologies in forecasting Parkinson's disease and offers valuable insights into the crucial predictors for PD prognosis. The results of our study could potentially contribute to the advancement of diagnostic methods for the timely identification of PD, with increased precision and efficacy.
Copyright © 2023 M. Shivakoti et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NCSA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.