Monitoring and Diagnosis of Neurodegenerative Diseases through Advanced Sensor Integration and Machine Learning Techniques

Neurodegenerative diseases, such as Alzheimer’s, Parkinson’s, and Multiple Sclerosis, are complex disorders that present significant challenges in both diagnosis and progression tracking. This paper explores innovative methods of healthcare monitoring for neurodegenerative diseases through the integration of advanced sensor technology with machine learning algorithms. Specifically, this study presents a comparative analysis of two models that leverage sensor data to enhance early detection and provide continuous patient monitoring. By implementing a dual-model approach, we seek to improve diagnostic accu-racy and predictive capabilities, paving the way for more personalized patient care and timely intervention. Extensive simulations were performed to evaluate the efficacy of these models, examining key metrics to compare performance. The results suggest that the integrated approach can offer substantial benefits over traditional diagnostic methods. Key insights from the simulation results are provided to guide future research and implementation in healthcare settings.