Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{174609,
        author = {Yash Thaware and V. N. Mahawadiwar and Nayan Aswar and Ayush Patre},
        title = {THE PARKINSON’S DISEASES PREDICTION USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {209-214},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174609},
        abstract = {The rising incidence of Parkinson’s Disease (PD) poses a critical challenge to public health, requiring early diagnosis for effective management. Traditional diagnostic methods, including clinical evaluations and specialized medical tests, are often expensive, time-consuming, and subjective, necessitating the development of automated and accurate predictive models. This study leverages Support Vector Machine (SVM) for classifying individuals into healthy and PD-affected categories. The methodology encompasses dataset acquisition, preprocessing, feature selection, model training, evaluation, and performance visualization. A comprehensive dataset consisting of voice recordings and biomedical attributes was utilized, partitioned into training and testing sets. Preprocessing steps involved normalization, feature scaling, and handling missing values to enhance model efficiency. Feature selection techniques such as Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE) were employed to improve classification accuracy by reducing dimensionality. A radial basis function (RBF) kernel-based SVM model was implemented, fine-tuned using grid search and cross-validation to optimize hyperparameters. Performance was assessed using accuracy, precision, recall and F1-score. The findings demonstrate that SVM effectively classifies Parkinson’s disease, particularly when coupled with feature selection and optimized hyperparameters. The proposed system offers a cost-effective, scalable, and accurate diagnostic aid, assisting medical professionals in early detection and treatment planning. This research enhances the application of machine learning in healthcare, contributing to automated and efficient Parkinson’s disease prediction.},
        keywords = {Support Vector Machine (SVM), Feature Selection, Machine Learning.},
        month = {March},
        }