Explainable Machine Learning for Cardiovascular Disease Risk Stratification Using Electronic Health Records: An XGBoost–SHAP Framework

Weiguo Gee

Explainable Machine Learning for Cardiovascular Disease Risk Stratification Using Electronic Health Records: An XGBoost–SHAP Framework

Weiguo Gee
Volume 7

Abstract

Medical image segmentation plays a fundamental role in computer-aided diagnosis, treatment planning, surgical navigation, radiotherapy guidance, and longitudinal disease monitoring. However, developing robust deep learning segmentation models typically requires access to large and diverse medical imaging datasets collected from multiple healthcare institutions. In real-world clinical environments, direct centralization of medical images is often restricted due to patient privacy regulations, institutional data-governance policies, ethical constraints, and concerns related to data ownership.

Keywords

References

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