Prognoza: Parkinson’s Disease Prediction Using Classification Algorithms

Parkinson's Disease (PD) is a persistent neurological condition that has global impact on significant number of individuals. The timely detection PD imperative for the efficacious treatment and control condition. Machine learning (ML) methods have demonstrated potential in forecasting disease based diverse data sources recent times. present research paper outlines study employs machine [ML]techniques to predict disease. A dataset comprising clinical demographic characteristics both patients diagnosed with healthy individuals was taken from Kaggle. aforementioned utilized train assess multiple models. experimental findings indicate CatBoost model exhibited superior performance compared other models, achieving an accuracy rate 95.1% root mean squared error 0.34.In summary, our showcases capabilities methodologies offers valuable insights into crucial predictors prognosis. results could potentially contribute advancement diagnostic identification PD, increased precision efficacy.