Outcomes
β¦ Enhanced Maintainability: The transition to Python resulted in more maintainable and readable code, making it easier for developers to understand and modify.
β¦ Improved Performance: Optimisation techniques applied during refactoring led to faster execution times and more effective handling of larger datasets.
β¦ Better Integration: The refactored algorithms integrate seamlessly with modern data analysis tools, enabling more sophisticated analysis and visualisation.
β¦ Increased Accuracy: The updated algorithms detect Parkinson’s disease parameters accurately, contributing to better patient monitoring and research outcomes.
β¦ Future-Proofing: By adopting Python, a widely used and supported language, the algorithms are now future-proofed, ensuring long-term usability and support.
This project demonstrates our capability to modernise complex systems, improve their performance, and ensure their continued relevance in a rapidly evolving technological landscape.