TY  - JOUR
AU  - Lolak, Sermkiat
AU  - Attia, John
AU  - McKay, Gareth J
AU  - Thakkinstian, Ammarin
PY  - 2023
DA  - 2023/7/26
TI  - Comparing Explainable Machine Learning Approaches With Traditional Statistical Methods for Evaluating Stroke Risk Models: Retrospective Cohort Study
JO  - JMIR Cardio
SP  - e47736
VL  - 7
KW  - stroke
KW  - machine learning
KW  - risk prediction model
KW  - explainable artificial Intelligence
KW  - risk factor
KW  - cohort study
KW  - high-risk patient
KW  - hypertension
AB  - Background: Stroke has multiple modifiable and nonmodifiable risk factors and represents a leading cause of death globally. Understanding the complex interplay of stroke risk factors is thus not only a scientific necessity but a critical step toward improving global health outcomes. Objective: We aim to assess the performance of explainable machine learning models in predicting stroke risk factors using real-world cohort data by comparing explainable machine learning models with conventional statistical methods. Methods: This retrospective cohort included high-risk patients from Ramathibodi Hospital in Thailand between January 2010 and December 2020. We compared the performance and explainability of logistic regression (LR), Cox proportional hazard, Bayesian network (BN), tree-augmented Naïve Bayes (TAN), extreme gradient boosting (XGBoost), and explainable boosting machine (EBM) models. We used multiple imputation by chained equations for missing data and discretized continuous variables as needed. Models were evaluated using C-statistics and F1-scores. Results: Out of 275,247 high-risk patients, 9659 (3.5%) experienced a stroke. XGBoost demonstrated the highest performance with a C-statistic of 0.89 and an F1-score of 0.80 followed by EBM and TAN with C-statistics of 0.87 and 0.83, respectively; LR and BN had similar C-statistics of 0.80. Significant factors associated with stroke included atrial fibrillation (AF), hypertension (HT), antiplatelets, HDL, and age. AF, HT, and antihypertensive medication were common significant factors across most models, with AF being the strongest factor in LR, XGBoost, BN, and TAN models. Conclusions: Our study developed stroke prediction models to identify crucial predictive factors such as AF, HT, or systolic blood pressure or antihypertensive medication, anticoagulant medication, HDL, age, and statin use in high-risk patients. The explainable XGBoost was the best model in predicting stroke risk, followed by EBM. 
SN  - 2561-1011
UR  - https://cardio.jmir.org/2023/1/e47736
UR  - https://doi.org/10.2196/47736
UR  - http://www.ncbi.nlm.nih.gov/pubmed/37494080
DO  - 10.2196/47736
ID  - info:doi/10.2196/47736
ER  -