Comparative Analysis of Machine Learning Algorithms for Accurate Parkinson’s Disease Detection

Parkinson’s disease (PD) is a progressive neurodegenerative disorder affecting millions globally. Early diagnosis is vital in managing the condition and improving patient outcomes. This study investigates the use of machine learning algorithms to detect Parkinson’s disease based on various biomedical features. A comparative analysis was performed using several classification algorithms, including AdaBoostClassifier, GradientBoostingClassifier, KNeighborClassifier, LGBMClassifier, and Random Forest Classifier, to identify patterns in predictive outcomes. Among these, the KNeighborClassifier achieved the highest accuracy of 95%. The study further compares precision, recall, and F1 scores across the algorithms, highlighting the potential of machine learning in clinical applications for Parkinson’s disease detection. Additionally, the research emphasizes the importance of exploring advanced deep-learning techniques to enhance prediction accuracy.