An Edge-Enabled IoT Framework for Real-Time Stroke Risk Prediction Using Explainable Machine Learning

Abstract

One of the most common causes of death and long-term disability, stroke requires immediate detection. The current stroke prediction methods based on traditional machine learning methods are mostly based on offline datasets and the IoT-based healthcare systems mainly detect and monitor healthcare data without performing intelligent prediction. This study proposes a conceptual framework based on the Internet of Things (IoT) concept. Digital twins (IoT) and real-time health monitoring, edge-computing and explainable machine learning to dynamically forecast strokes. The suggested framework consists of a number of wearable sensors that continually monitor several physiological indicators such as heart rate, blood pressure, glucose level, and body mass index. The preliminary data preprocessing and anomaly filtering are done in an edge computing layer and the real-time stroke risk prediction is done by a cloud-based machine learning module. Especially, the framework includes explainable AI methods to enhance the understanding and trust in clinical decisions. The proposed system can offer “interpretable predictions” and low latency processing, and have continuous monitoring capabilities that make them suitable for next-generation smart healthcare applications when compared with conventional systems.