Predictive Analytics of Stroke Patients Monitoring System using Deep Learning Models

Design a risk predictive stroke warning and monitoring system via Internet of Things-enabled devices integrating IoT with a supervised deep learning framework for detecting patients at higher stroke risk who in time receive proactive care.System devices use sensing through body temperature and heart rate together with SpO2 levels from IoT-enabled sensing apparatus connected through the microcontroller of the present application. This would apply advanced algorithms such as InceptionV3 for analyzing medical images and compares the performance to other models that may include even CNNs. Data would be processed, analyzed and monitored in a cloud-based environment where there are alerts issued when there is a critical health condition.The results show that the InceptionV3 Algorithm obtained the best accuracy and reliability with an accuracy of 94.3% in stroke risk prediction among all tested algorithms. The system was very efficient and scalable for remote healthcare applications. It could monitor live health data and provide early warning alerts that helped support timely and effective healthcare interventions. Conclusion: This system offers a simple and affordable solution for stroke management through the combination of IoT and machine learning technologies. It improves early detection, ensures timely care and supports healthcare professionals, thereby enhancing patient outcomes while being adaptable to different healthcare settings.