Examining Machine Learning Methods for Accurate Parkinson’s Disease Identification

Parkinson’s disease (PD) is a prevalent neurode-generative ailment that highlights the crucial need of early detection in order to provide successful therapy. Parkinson’s disease is primarily caused by the progressive loss of dopamine-producing neurons in the brain. This study investigates the use of Machine Learning models such as Logistic Regression, K-Nearest Neighbors ,Decision Tree, Random Forest, XGBoost, CatBoost, and LightGBM for PD identification. The prevalence of Parkinson’s disease underscores the critical necessity for early identification and quick treatments. While Logistic Regression, KNN, Decision Tree, Random Forest, CatBoost, and XGBoost all contribute to the model’s robustness, LightGBM achieves the top accuracy rate at 97.52%, outperforming the other models. This study not only presents a reliable technique for early Parkinson’s disease identification, but it also highlights the possibility of merging multiple machine learning models to enhance diagnostic outcomes, ultimately contributing to advances in neurology and customized healthcare.