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@article{170794,
        author = {A.V.L. Prasuna and Sure Sree Charan Reddy and Toom Ritesh Reddy},
        title = {Classification And Prediction Of Parkinson’s Disease Using Machine Learning Algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {2506-2510},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170794},
        abstract = {Millions of people worldwide suffer from Parkinson's disease (PD), a degenerative neurological disorder. Better results and an efficient intervention depend on early and precise detection. The development of prediction models using machine learning methods like AdaBoost, Random Forest, and Decision Trees is the main goal of this study. Performance indicators such as accuracy, precision, recall, and F1-score were used to evaluate these models using datasets made up of spiral and wave drawings that were taken from motor skill assessments. Random Forest showed the best prediction performance and the highest dependability of all the models examined. These findings demonstrate how machine learning can be used to develop automated methods that aid in PD early diagnosis.},
        keywords = {AdaBoost, Random Forest, Decision Tree, Early Diagnosis, Machine Learning, Parkinson's Disease},
        month = {December},
        }