Parkinson's Disease Diagnosis using Neutral Matrix and Machine Learning

This research explores the potential of the Neutral Matrix (NM) to diagnose Parkinson's disease (PD). It builds upon current diagnostic methods by incorporating imaging, genetic markers, and advanced clinical data analysis. The findings highlight the effectiveness of NM in achieving accurate and consistent disease identification, potentially improving patient satisfaction and enabling early intervention. This study emphasizes the significance of NM as a contributor to the ongoing development of PD diagnostic strategies. By enhancing the accuracy and reliability of diagnosis, NM offers valuable data-driven insights for timely identification of this debilitating condition. Ultimately, this research paves the way for improved PD diagnosis and potentially improved quality of life for affected individuals, leveraging the valuable resource of NM. Additionally, the study delves into the application of game-based assessments in PD diagnosis. By demonstrating the potential of a data-driven approach for accurate and early PD detection, the research contributes significantly to the scientific field. The methodology, trends, operational techniques, results, discussion, and implications are outlined in detail within the study.