High-Dimensional Regression Modeling of Neurological Biosignal for Real-Time Predictive Diagnostics in e-Health Platform

Abstract

Neurological conditions affect one billion people worldwide, causing mobility issues, and these conditions often persist even after treatment. Early prediction is crucial to improving patient outcomes and enabling prompt intervention, while late prediction is a significant health concern for neurological diseases. The quick development of e-health technology has opened up new possibilities for biosignal-analytics-based early disease prediction and ongoing neurological monitoring. Advances in machine learning (ML) are making it possible for medical professionals to use complex biomedical data to more effectively and creatively predict the earlier stages of neurological diseases. Additionally, by identifying particular seizure patterns in brain waves, EEG-based ML models have demonstrated efficacy in the diagnosis of epilepsy. These examples highlight how ML can improve diagnostic precision and support medical practitioners in making decisions. Many obstacles still exist in spite of these developments. Model generalization may be hampered by the variety, noise, and small size of neurological data. Therefore, this study introduces a high-dimensional regression modeling framework for real-time predictive diagnostics of neurological disorders using multimodal biosignals such as electromyography (EMG), electroencephalography (EEG), and electrocardiography (ECG). The suggested system effectively processes massive, high-frequency biomedical data streams by fusing cutting-edge machine learning algorithms with cloud-enabled e-health infrastructure. To manage the signal complexity, increase prediction accuracy, and reduce redundancy, dimensionality reduction and feature extraction techniques are used. Predictive models are used to evaluate neurological risk patterns in real time and find clinically significant biomarkers. When compared to conventional diagnostic systems, experimental evaluation shows increased scalability, decreased computing delay, and higher prediction accuracy. Accessibility and reliability in remote healthcare settings are further improved by the combination of wearable biosensors, edge computing, and secure cloud communication.