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

I tiakina i:
I whakaputaina i:Journal of Medical and Biological Engineering vol. 44, no. 6 (Dec 2024), p. 799
Kaituhi matua: Argymbay, Mariyam
Ētahi atu kaituhi: Khan, Shams, Ahmad, Noman, Salih, Mira, Mamatjan, Yasin
I whakaputaina:
Springer Nature B.V.
Ngā marau:
Software
Datasets
Recommender systems
Applications programs
Body mass index
Gender
Risk factors
Cholesterol
Feature selection
Machine learning
Explainable artificial intelligence
Risk assessment
Learning algorithms
Patients
Smartwatches
Large language models
Heart attacks
Blood pressure
Classification
Glucose
Wearable computers
Natural language processing
Ischemia
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Whakarāpopotonga:PurposeWe 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.MethodsThis 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.ResultsThe 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.ConclusionAccording 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.
ISSN:1609-0985
2199-4757
1019-0465
DOI:10.1007/s40846-024-00922-3
Puna:Health & Medical Collection