Early Detection of Parkinson's Disease: Ensemble Learning for Improved Diagnosis

All over the world, 55% of old age people have Parkinson's disease. The patient faces problems in speech and mobility, and it is difficult to get physical treatment and observation to patients. It is necessary to detect the symptoms of Parkinson's earlier automatically, yet traditional diagnostic methods often lack accuracy. This paper proposed several machine learning algorithms such as Decision Tree, Random Forest, Logistic Regression and Support Vector Machine and design an ensemble of these models to detect and classify Parkinson's disease. This study used a standard benchmark Parkinson's dataset. The SMOTE technique addresses the problem of misbalancing the data. The decision tree extracts the relevant features from the dataset. The final result of the ensemble model achieves a 96.62% accuracy score, which is better than other baseline classifiers. This research highlights the potential of these advanced techniques in clinical settings.