• Kalaiyarasi R
• Dr. Jasmine J
• BanuPriya M

Parkinson's Disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms, affecting millions of individuals worldwide. Early and accurate prediction of the disease progression is crucial for effective management and personalized treatment plans. This study explores the application of the Random Forest (RF) algorithm as a predictive tool for assessing the progression of Parkinson's Disease. The dataset used in this research comprises a comprehensive collection of clinical and demographic features obtained from individuals diagnosed with Parkinson's Disease. Leveraging the versatility and efficiency of the Random Forest algorithm, we employ a machine learning approach to analyze the complex relationships among various factors influencing the disease progression. Our methodology involves data preprocessing, feature selection, and model training using the Random Forest algorithm. The RF algorithm, known for its ability to handle high- dimensional data, nonlinearity, and interactions among features, is employed to build a predictive model. The model is fine-tuned through cross validation and performance metrics to ensure robustness and generalizability. The results of our analysis demonstrate the effectiveness of the Random Forest algorithm in predicting Parkinson's Disease progression. We identify key contributing factors and their relative importance in the prediction process. The developed model exhibits high accuracy, sensitivity, and specificity, showcasing its potential as a valuable tool for clinicians in prognostic evaluations.

Parkinson's disease (PD), Random Forest, Machine learning, Feature selection

"HARNESSING AI FOR EARLY PARKINSONS DISEASE PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.f220-f224, April-2024, Available :http://www.jetir.org/papers/JETIR2404527.pdf

"HARNESSING AI FOR EARLY PARKINSONS DISEASE PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppf220-f224, April-2024, Available at : http://www.jetir.org/papers/JETIR2404527.pdf