Classification Of Parkinson's Disease Using Machine Learning Technique

Parkinson’s disease (PD) progresses through the nervous system as a neurodegenerative condition which causes severe effects on motor function and generates life quality problems. Standard diagnostic approaches face two main limitations that include a subjective basis and prolonged diagnostic delays. This research develops an AI-driven diagnosis classification system which involves analyzing datasets from public domains together with medical patient information stored at Baghdad hospitals. The decision tree and random forest models together with SVM and CNN provided implementation for PD diagnosis assessment through performance metrics testing. The CNN model achieved the highest correctness rate of 94.3% as well as precision of 93.1% and F1-score of 93.5% to outperform the other proposed models during testing. The application of CNN on the local dataset achieved a strong 90.7% accuracy because it successfully adapted to different data quality and format conditions. The research determined that tremor frequency and voice pitch variation together with hand movement speed proved to be the most effective diagnostic features. Research demonstrates AI diagnostic technologies can assist early Parkinson disease identification at healthcare sites which lack resources by showing the necessity of better digital networks and standardized research data alongside professional education for medical staff.