Investigation on Enhanced Detection of Parkinson's Disease in Brain Imaging Using Ensemble Learning Approaches

Masses of people around the world are affected by Parkinson's disease (PD), a progressive neurological disorder. Timely and precise diagnosis is critical for effective management. Yet conventional diagnostic methods often lack compassion and specificity. Advances in machine learning and cerebral imaging offer promising approaches to improve PD diagnosis. This investigation investigates an ensemble machine learning move toward to enhance the identification of Parkinson's disease using cerebral imaging data. By integrating models such as decision trees (DT), support vector machines (SVM), and neural networks, the ensemble method combines their strengths to create a reliable diagnostic system. Feature extraction and preprocessing further enhance interpretability and performance. Compared to individual models, the ensemble approach significantly improves diagnostic accuracy, sensitivity, and specificity. Brain imaging aids differential diagnosis in Parkinsonism, but no Cerebral imaging methods is currently recommended for routine clinical practice despite their proven utility. Larger trial are needed to validate the medical value and long-standing cost-effectiveness of Cerebral imaging for managing patients within healthcare systems like the NHS.