Abstract :

Parkinsons Disease Is A Progressive Neurological Disorder That Affects Motor Functions Due To The Degeneration Of Dopamineproducing Cells In The Brain. Early Identification Of The Disease Is Important For Improving Diagnosis And Patient Management. This Project Presents A Machine Learning-based Approach For Predicting Parkinsons Disease Using Clinical And Voice-related Data Obtained From The UCI Machine Learning Repository. The Collected Dataset Undergoes Preprocessing, Including Normalization And Feature Selection, To Retain The Most Relevant Attributes For Classification. The Proposed System Employs The XGBoost Classifier To Analyze The Processed Data And Predict The Presence Of Parkinsons Disease. Model Performance Is Evaluated Using Standard Classification Metrics On Training And Testing Datasets. The Developed Model Achieved A Training Accuracy Of 100% And A Testing Accuracy Of 97.25%, Along With High Precision, Recall, And F1-score, Indicating Effective Classification Performance. The Results Demonstrate That The Proposed Approach Can Accurately Identify Patterns Associated With Parkinsons Disease And May Support Early Diagnosis And Clinical Decision-making. Keywords— Parkinsons Disease, XGBoost, Machine Learning, Clinical Data, Disease Prediction, Classification.

Published:

07-6-2026

Issue:

Vol. 26 No. 6 (2026)

Page Nos:

1919 - 1924

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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