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

Lesia Mochurad1, Viktoriia Babii2, Yuliia Boliubash2

  • 1Artificial Intelligence Department, Lviv Polytechnic National University, 12 S. Bandery St, Lviv, 79013, Ukraine. lesia.i.mochurad@lpnu.ua.

BMC Medical Informatics and Decision Making
|February 7, 2025
PubMed
Summary

This study enhances stroke risk prediction using XGBoost and principal component analysis (PCA) with explainable artificial intelligence (XAI). The novel approach improves model accuracy and interpretability for better healthcare forecasting.

Keywords:
Class balancingMachine learningPCA methodParallel computing technologiesSHAP method

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Computational Biology

Background:

  • Stroke is a leading cause of disability and mortality globally.
  • Existing stroke risk prediction models require improved efficiency and interpretability.
  • Cerebrovascular diseases represent a significant public health concern.

Purpose of the Study:

  • To enhance stroke risk prediction models by integrating advanced machine learning and dimensionality reduction techniques.
  • To improve the efficiency and interpretability of stroke risk assessment.
  • To introduce explainable artificial intelligence (XAI) into the principal component analysis (PCA) process for better risk factor understanding.

Main Methods:

  • Utilized XGBoost and optimized principal component analysis (PCA) for data structuring and processing.
  • Integrated explainable artificial intelligence (XAI) into PCA for enhanced transparency and interpretation.
  • Employed OpenMP parallelization to accelerate processing speed.
  • Validated the approach on two distinct datasets.

Main Results:

  • Achieved high prediction accuracy of 95% and 98% on tested datasets.
  • Demonstrated excellent model generalizability and reliability with cross-validation yielding an average of 0.99.
  • Obtained high Matthew's correlation coefficient (MCC) of 0.96 and Cohen's Kappa (CK) of 0.96.
  • Increased processing speed threefold through OpenMP parallelization.

Conclusions:

  • The proposed method offers an innovative and reliable approach to stroke risk prediction.
  • Integration of XAI with PCA enhances the interpretability of risk factors for medical professionals.
  • The threefold increase in processing speed makes the model practical for real-world healthcare applications.
  • This approach has the potential to significantly improve forecasting systems in the healthcare industry.