A Comprehensive Ensemble Machine Learning Model for Predicting Parkinson’s Disease Progression and Severity

A progressive neurological disease that impacts millions of people worldwide is Parkinson's disease. For illness care to be effective and for patient outcomes to be improved, early and correct diagnosis is essential. The durable ensemble algorithmic learning model presented in this paper is intended to identify Parkinson's disease with an elevated level of accuracy. The strategy leverages the strengths of several algorithms while limiting their particular flaws by integrating many unique classifiers through a deliberately weighted voting system. To train and assess the ensemble, an extensive dataset was utilized that included neuromotor, biochemical, and clinical variables. To find the most predictive qualities and decrease model complexity while increasing computing efficiency, feature selection was done. The ensemble method outperformed single predictor models, achieving an amazing 96% accuracy in identification. In the validation state, the proposed model worked well on the data which is different from the training data. The combined machine learning model approach will help to find the disease in the early stages and improve the effectiveness of the clinical decisions. The diagnostic techniques can be made better with this approach which makes the healthcare sector more stable and accurate. This study has a significant impression that can be widely used in the healthcare field.