An Intelligent Framework for Real-Time Heart Stroke Monitoring and Early Detection Using Machine Learning

The interruption of blood flow resulting from sudden brain function shutdown leads to the development of heart stroke, which represents a life-threatening medical emergency. Traditional healthcare approaches demonstrate limited capability for delivering prompt insights together with forecasts, thus limiting their support for stroke case real-time decision support systems. Cyber-physical systems (CPS) form an innovative framework to boost heart stroke prediction along with monitoring capabilities during the early stages. The system contains physical and cyber components as its main two parts where physical devices active in real-time analyze biological occurrences, while cyber components complete advanced assessments for decisive operations. Pre-processing operations including data balancing and noise reduction and feature selection are carried out by the system through the execution of random forest and artificial neural networks machine learning algorithms. Kruskal-Wallis H-test serves as the statistical methodology to validate the integrated dataset by establishing robustness measures in its integration. The framework achieves accurate heart stroke predictions based on its evaluation measures, which include accuracy, precision, recall, and F1-score. The framework achieves accurate heart stroke predictions based on its evaluation measures, which include accuracy (95.7%), precision (94.3%), recall (93.8%), and F1-score (94.0%). Statistical validation through Kruskal-Wallis H-test (p < 0.01) confirms the framework's robust performance in prediction tasks. This framework consists of two analysis layers that provide time-sensitive heart stroke monitoring capabilities. Advanced machine learning enhances the prediction and forecasting accuracy for heart stroke cases. Statistical validation and evaluation metrics confirm the framework's robust performance in prediction tasks. The results highlight the CPS-based approach as a significant advancement, contributing to improved predictive capabilities and facilitating more personalized patient care.