Parkison Disease Prediction using Machine Learning In Healthcare

Detecting Parkinson's Disease at early stages remains difficult because the symptoms begin slowly at first. The investigation analyzes how machine learning (ML) strengthens diagnostic precision by using vocal biomarkers. We have applied supervised learning algorithm assessment on public voice data by using Random Forest along with ensemble models while performing standard classification measurements. Random Forest using Entropy as well as Voting Classifier provided the most effective results for identifying participants with PD among those without PD. The study demonstrates that ML-based voice analysis represents an accessible and non-obtrusive method to help clinicians make early PD diagnoses and make clinical decisions.