A Smart Recommender System for Stroke Risk Assessment with an Integrated Strokebot

Abstract

Purpose

We present a machine learning-based online recommendation system for stroke risk assessments. With this tool, users will be able to take proactive steps in managing their health by predicting stroke risk based on diverse data input, providing transparent and reliable risk factor interpretations, and helping healthcare professionals make informed clinical decisions.

Methods

This study uses the publicly available Stroke Analysis dataset. To predict stroke risk, the CatBoost classifier is employed, while the XAI component incorporates SHAP explainer to provide insights into its reasoning. A Django-based web application allows users to upload risk factor data and receive personalized stroke risk predictions. Smartwatch integration allows continuous monitoring of dynamic risk factors. BioMistral 7B Large Language Models (LLM) is employed to create an intuitive AI medical assistant.

Results

The developed automated online recommender system is highly accurate and robust for stroke risk assessment. The CatBoost classifier shows an average AUC of 0.98. In addition to the SHAP explainer, the recommender system also integrates Google Maps, Alert System, and Q/A chatbot based on LLMs.

Conclusion

According to the study, AI-driven systems can assist in stroke risk assessment and preventive care strategies. Developing a user-friendly online recommender system provides proof of principle for an efficient and user-friendly health management tool using machine learning, explainable AI, and LLM.