Application of Computer Vision in the Diagnosis Parkinson's Disease

In the field of neurology and medical diagnostics, Parkinson's disease (PD) must be identified as soon as possible. In its early stages, modest handwriting abnormalities and anomalies in repetitive tasks, such spiral drawing, are common detectable indicators of Parkinson's disease (PD). This study confirms spiral drawings' importance as a useful tool for tracking Parkinson's disease development over time. Advanced statistical software analysis and machine learning (ML) approaches have been used to evaluate and understand pertinent datasets in the context of diagnosing Parkinson's disease. The study concentrates on the implementation of Boosting algorithms, a popular machine learning approach that makes use of an ensemble of decision trees, in order to get accurate and efficient results. This methodology facilitates the retrieval of significant insights from intricate datasets and aids in the prompt identification and observation of Parkinson's disease. When combined with thorough data analysis, the use of Boosting algorithms improves the precision of Parkinson's disease diagnosis and serves as a basis for the creation of prediction models and therapeutic approaches. Through early intervention and better management, this initiative offers a viable option for improving the lives of those affected by Parkinson's disease. It also highlights the potential of data-driven techniques in this sector.