Department of Computer Science and Biomedical Engineering Research Centre, University of Cyprus, Nicosia, Cyprus.
CYENS Centre of Excellence, Nicosia, Cyprus.
Stud Health Technol Inform. 2024 Aug 22;316:978-982. doi: 10.3233/SHTI240574.
The objective of this study was to develop explainable AI modeling in the prediction of cardiovascular disease. The XGBoost algorithm was used followed by rule extraction and argumentation theory that provides interpretability, explainability and accuracy in scenarios with low confidence results or dilemmas. Our findings are in agreement with previous research utilizing the XGBoost machine learning algorithm for prediction of cardiovascular risk, however it is supported by rule based explainability, offering significant advantages in terms of providing both global and local explainability. Further work is needed to enhance the argumentation-based rule interpretability, explainability and accuracy in scenarios with low confidence results or dilemmas.
本研究旨在开发可解释人工智能模型，用于预测心血管疾病。我们使用 XGBoost 算法，结合规则提取和论证理论，为置信度低或存在困境的情况提供可解释性、可说明性和准确性。我们的研究结果与之前利用 XGBoost 机器学习算法预测心血管风险的研究一致，但通过基于规则的可解释性得到支持，在提供全局和局部可解释性方面具有显著优势。需要进一步的工作来提高基于论证的规则可解释性、可说明性和准确性，以应对置信度低或存在困境的情况。
