Parkinsons Disease Classification

Abstract

Parkinson's ailment is a degenerative ailment that affects movement, making early diagnosis critical for higher treatment and management. In this study, we assess the running performance of various machine studying fashions—Logistic Regression, Random Forest, XGBoost, aid Vector Classifier, K-Nearest buddies, Naive Bayes, and selection trees—on voice facts for Parkinson's ailment category. techniques consisting of (EDA), function scaling, and pass-validation have been conducted to enhance model overall performance. To address the magnificence imbalance, we used SMOTE, and version assessment became primarily based on accuracy, precision, F1-score metrics. XGBoost appeared because of the nice version, accomplishing 93.33% accuracy and an F1-score of 95.65%, successfully distinguishing PD sufferers and healthy people. This observation demonstrates the potential of ML in assisting early analysis of Parkinson's sickness, imparting clinicians with a precious tool to improve patient results.