Stroke is a complicated emergency event that can be diagnosed at a hospital. It leads to major neurological impairments and patient disability. It is imperative to have a smart system that can help with early stroke prediction or detection and hence assist clinicians in stroke risk management.
The Smart Health Recommender System effectively employs machine learning and the use of explainable artificial intelligence in order to create a framework for stroke risk monitoring and thereby prevention. Along with predicting and explaining patient results it also provides an interface for management of stroke risk wherein patients can track their risk level analysis live in real-time also. This system is meant to help high stroke risk individuals such as people who suffered a transient ischemic attack efficiently monitor their risk levels and hence be able to take the requisite precaution.
The results from the study show that this Smart Health Recommender System can predict the stroke risk levels and subsequently procure meaningful insights on the stroke risk factors that can assist clinicians for better managing stroke risk based on the proposed framework.