Improving stroke risk prediction by integrating XGBoost, optimized principal component analysis, and explainable artificial intelligence

Improving stroke risk prediction by integrating XGBoost, optimized principal component analysis, and explainable artificial intelligence

Abstract The relevance of the study is due to the growing number of diseases of the cerebrovascular system, in particular stroke, which is one of the leading causes of disability and mortality in the world. To improve stroke risk prediction models in terms of efficiency and interpretability, we prop...

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Bibliographic Details
Main Authors: Lesia Mochurad, Viktoriia Babii, Yuliia Boliubash, Yulianna Mochurad
Format: Article
Language:English
Published: BMC 2025-02-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Machine learning
Parallel computing technologies
SHAP method
Class balancing
PCA method
Online Access:https://doi.org/10.1186/s12911-025-02894-z
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