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Comparing Explainable Machine Learning Approaches With Traditional Statistical Methods for Evaluating Stroke Risk

Sermkiat Lolak1, John Attia2, Gareth J McKay3

  • 1Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.

JMIR Cardio
|July 26, 2023
PubMed
Summary

Explainable machine learning models accurately predict stroke risk in high-risk patients. Extreme gradient boosting (XGBoost) and explainable boosting machine (EBM) showed the best performance in identifying key risk factors.

Keywords:
cohort studyexplainable artificial Intelligencehigh-risk patienthypertensionmachine learningrisk factorrisk prediction modelstroke

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Area of Science:

  • Cardiovascular disease research
  • Machine learning in healthcare
  • Public health and epidemiology

Background:

  • Stroke is a leading global cause of death with numerous risk factors.
  • Understanding stroke risk factor interplay is crucial for improving health outcomes.

Purpose of the Study:

  • To evaluate explainable machine learning models for stroke risk prediction.
  • To compare their performance against traditional statistical methods using real-world data.

Main Methods:

  • Retrospective cohort study of high-risk patients (2010-2020).
  • Compared logistic regression, Cox proportional hazard, Bayesian network, TAN, XGBoost, and EBM models.
  • Utilized C-statistics and F1-scores for model evaluation.

Main Results:

  • XGBoost achieved the highest C-statistic (0.89) and F1-score (0.80).
  • Key stroke predictors identified include atrial fibrillation (AF), hypertension (HT), and age.
  • AF, HT, and antihypertensive medication were significant across most models.

Conclusions:

  • Explainable XGBoost and EBM models effectively predict stroke risk in high-risk populations.
  • Identified critical factors like AF, HT, and medication use for targeted interventions.