Parkinson's Disease Analysis and Prediction Using Regression Models

Computer Science Engineering

Parkinson's Disease (PD) is a chronic and progressive neurodegenerative disorder that affects millions of individuals globally. It is characterized by a wide range of motor and non-motor symptoms, including tremors, bradykinesia, rigidity, and postural instability. The proposed system for Parkinson's disease analysis and prediction aims to overcome the limitations of the existing system by leveraging advanced technologies and methodologies. It incorporates comprehensive data collection methods, advanced analytics techniques, interactive visualization tools, seamless integration with clinical workflows, scalability, security measures, and a user-friendly interface. The system offers personalized predictive analytics capabilities, enabling more accurate predictions, personalized treatment planning, and improved patient outcomes. By computer programs we can analyze data which can help to predict how severe Parkinson's Disease might be for a person. We tested different methods, like looking for patterns in the data and using a type of artificial intelligence called neural networks. These methods were pretty good at predicting the severity of Parkinson's Disease. This project not only serves as a valuable tool for predicting Parkinson's Disease progression but also facilitates data driven insights and informed decision-making in clinical practice. Moving forward, further refinements and enhancements can be made to improve model accuracy, expand data analysis capabilities, and incorporate additional features to address the evolving needs of healthcare stakeholders.

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