Comprehensive Study: Advancements in Parkinson's Disease Diagnosis with Data-Driven Insights and Machine Learning
S M Subhashini, Suthir Sriram, V Nivethitha
Abstract
The authors have a unique strategy that has never been used before, suggesting that a wearable device that records data is a combination of data from patients with Parkinson's disease and healthy individuals. Thus, it can accurately record motor features that are especially prevalent in Parkinson's disease, allowing for the actual identification of motor patterns peculiar and symptom intensity assessments. To further innovate in the field of self-assessment and individual evaluation of a patient's Parkinson's disease progression, the study points a significant relationship between clinical tests and quantitative symptom scores. Finally, a planned second opinion system using sensors and machine learning would help reduce the number of people misdiagnosed by comparing individuals with Parkinson's disease and those with similar phenotypic diseases. The differential diagnosis of tremor utilizing machine learning algorithms has been validated in various patient cohorts, significantly enhancing diagnostic specificity. In addition, the current study underscores the game-changing aspect of wearable technology and machine learning for medical care, eliminating resource constraints and allowing for the early identification and treatment of diseases. The experiment aims to improve remedy results and PD sufferers lives by recognizing barriers and encouraging ongoing research and innovation. It features the importance of patient-oriented monitoring methods, which are among the most valuable for PD patients with advanced disease. These techniques integrate quantitative sensor data analysis with handwritten comments and remote video monitoring. Finally, the investigation promotes tailored approaches to diverse patient demands, achieving better PD intervention and care capabilities.