Parkinson’s Disease (PD)

Parkinson’s Disease (PD) is a progressive neurolog- ical disorder that affects millions of people around the world. Detecting it early can make a huge difference in how well patients respond to treatment and manage their symptoms. In this study, we explored how machine learning (ML) can help identify PD earlier and more accurately by analyzing clinical and genetic data.We gathered a rich dataset that included detailed medical and genetic information from both individuals diagnosed with PD and healthy participants. Using this data, we applied several ma- chine learning techniques—specifically Random Forest, Decision Tree, and AdaBoost—to build predictive models for Parkinson’s Disease. We also used feature selection and engineering methods to improve the accuracy and efficiency of these models. Among the algorithms we tested, the Random Forest model stood out, achieving an impressive AUC-ROC score of 0.95. This means it was highly effective at distinguishing between PD patients and healthy individuals. Through feature importance analysis, we also identified specific clinical signs and genetic markers that played a major role in predicting the disease. Our findings show that machine learning can be a powerful tool in the early detection of Parkinson’s Disease. These models have the potential to support doctors in making more informed decisions, allowing for earlier intervention and more personalized care for those at risk.