Stroke Prediction Models Using ML

• Stroke prediction models are computational systems that integrate clinical and imaging data to assess individual risk and guide early intervention.
• They use rigorous data preprocessing, feature selection, and imbalance correction to address challenges such as missing values and skewed outcomes.
• Advanced ensemble and deep learning techniques achieve high accuracy and sensitivity, enhancing personalized and population-level stroke risk assessment.

Stroke prediction models using machine learning constitute a dynamic and technically rich research area intersecting computational modeling, clinical informatics, and population health. Predictive modeling aims to quantify stroke risk in individual patients or populations using demographic, clinical, laboratory, and imaging data, ultimately facilitating early intervention, improved outcomes, and optimized resource allocation.

1. Data Sources, Clinical Domains, and Risk Factors

Stroke prediction model development leverages both tabular and imaging-based datasets. Population screening is typically performed using tabular features sourced from hospital records, EHR data, public disease surveillance (e.g., CSPP in China), or research cohorts such as MIMIC, CHARLS, and multiple Kaggle datasets (Liu et al., 2021, Fernandez-Lozano et al., 2024, Chadha, 2024, Hatami et al., 2023, Islam et al., 18 May 2025, Tashkova et al., 14 May 2025). Imaging-based models incorporate high-dimensional features from CT or MRI scans for acute diagnosis or prognostication (Hossen et al., 4 Jul 2025, Hatami et al., 2023).

Key variable categories and representative predictors include:

• Demographics: Age (cardinal non-modifiable risk driver), sex/gender, ethnicity.
• Clinical comorbidities: Hypertension, diabetes, atrial fibrillation, heart disease, CKD, history of stroke/TIA.
• Lifestyle and social determinants: Smoking status, physical inactivity, dietary factors, work type, residence (urban/rural).
• Clinical/lab measurements: Blood pressure, BMI, serum glucose, creatinine, BUN, prothrombin time, NIHSS/functional scores.
• Imaging biomarkers: Infarct volume, lesion texture, perfusion/diffusion features.

The selection and weighting of these features is often dataset-specific, but age, hypertension, CKD, diabetes, heart failure, BMI, and glucose levels consistently emerge as dominant predictors (Fernandez-Lozano et al., 2024, Letham et al., 2015, Islam et al., 18 May 2025, Akib et al., 1 Dec 2025, Pan et al., 15 Mar 2025).

2. Data Preprocessing, Feature Engineering, and Imbalance Correction

Medical datasets typically exhibit severe class imbalance (stroke event rates ≈ 2–8% in community screening, ≈16% ICU mortality), heterogeneous formats, and missingness. Preprocessing best practices, standardized across studies, involve:

• Missing value imputation: Random Forest imputation for tabular data; SVD-based for EHRs; mean/mode encoding for categorical gaps (Pan et al., 15 Mar 2025, Li et al., 2 Jun 2025, Tashkova et al., 14 May 2025).
• Data normalization: z-score or min–max normalization of continuous features. One-hot or label encoding for categorical variables to facilitate ML model compatibility.
• Outlier handling: IQR or z-score methods followed by winsorization (Islam et al., 18 May 2025, Akib et al., 1 Dec 2025).
• Collinearity reduction: Removal of features with high Pearson correlation (|r|>0.9), PCA on feature blocks, or LASSO-based selection (Pan et al., 15 Mar 2025, Akib et al., 1 Dec 2025).
• Imbalance correction: Synthetic Minority Over-sampling Technique (SMOTE), Random Oversampling (ROS), ADASYN for minority enrichment; sometimes undersampling majority class (Ismail et al., 2023, Jing, 2022, Tashkova et al., 14 May 2025, Li et al., 2 Jun 2025, Akib et al., 1 Dec 2025).
• Feature selection: Statistical filtering (point-biserial correlation, t-test), Gini impurity via Random Forests, regularization (LASSO), recursive elimination, and SHAP-based importance (Islam et al., 18 May 2025, Fernandez-Lozano et al., 2024, Pan et al., 15 Mar 2025, Li et al., 2 Jun 2025).

These steps are critical to avoid bias, overfitting, and spurious correlations, especially on small or highly imbalanced sets.

3. Model Architectures and Predictive Frameworks

Stroke prediction models span interpretable linear techniques, tree-based ensembles, deep neural architectures, and advanced ensemble methods:

• Logistic Regression: Baseline for binary outcome prediction; valued for interpretability and calibration (probabilities reflect clinical risk) (Chadha, 2024, Pan et al., 15 Mar 2025, Tashkova et al., 14 May 2025). Often used as a meta-learner in stacking frameworks.
• Tree-Based Methods: Decision Tree (CART), Random Forest (RF), Gradient Boosting Machines (GBM/LightGBM/XGBoost/CatBoost), and ExtraTrees are preferred for tabular data and high-order interaction capture (Ismail et al., 2023, Chadha, 2024, Akib et al., 1 Dec 2025, Tashkova et al., 14 May 2025). RF and XGBoost typically offer the best single-model AUCs (0.94–0.99 after balancing) (Ismail et al., 2023, Akib et al., 1 Dec 2025).
• Kernel Methods: Support Vector Machine (SVM), with RBF kernel and balanced class weights, can achieve high AUC in smaller outcome datasets and complex clinical cohorts (Pan et al., 15 Mar 2025, Dai et al., 2023).
• Deep Neural Networks: Dense neural nets (MLP), CNNs (for imaging or 1D tabular data), and hybrid architectures (AE-LSTM, xDeepFM) target high-dimensional or multimodal features (Hatami et al., 2023, Dai et al., 2023, Chadha, 2024, Hossen et al., 4 Jul 2025). Advanced frameworks leverage pre-trained CNN backbones plus supervised feature reduction (LDA/BFO/PCA) and SVC classifiers for imaging (Hossen et al., 4 Jul 2025).
• Hybrid/Ensemble Models: Stacking (meta-learning), weighted-voting, and probability fusion combine the strengths of multiple base learners (RF, XGBoost, SVM, LightGBM, MLP), often with logistic regression for final stratification (Islam et al., 18 May 2025, Akib et al., 1 Dec 2025, Zhiwan et al., 17 Apr 2025). Ensembles consistently outperform single models in test-set F1 and AUC, achieving up to 99% accuracy/AUC in balanced datasets (Akib et al., 1 Dec 2025).
• Rule-Based and Bayesian Models: Bayesian Rule Lists (BRL) confer interpretability in clinical deployment (short, sparse lists of conjunctions) with competitive accuracy (AUC ≈ 0.76 vs 0.72 for CHADS₂) (Letham et al., 2015).

A sample stacking ensemble is given by: $$P(y=1|x) = \sigma\left(\beta_0 + \sum_{m=1}^M \beta_m P_m(y=1|x)\right)$$ where $P_m$ are base model probabilities (Islam et al., 18 May 2025).

4. Model Evaluation, Performance Metrics, and Comparative Results

Rigorous evaluation uses stratified k-fold cross-validation or held-out test sets, emphasizing not only accuracy but also sensitivity (recall), specificity, AUC-ROC, and F1-score:

• Global performance: Single RF/XGBoost models: AUCs 0.85–0.99 (after data balancing) (Ismail et al., 2023, Pan et al., 15 Mar 2025, Akib et al., 1 Dec 2025). Ensembles/stacking further raise performance (ensemble AUC = 0.99–1.00, F1 = 0.97–0.99) (Islam et al., 18 May 2025, Akib et al., 1 Dec 2025).
• Sensitivity and false negatives: Logistic regression, when class-weighted, offers optimal sensitivity (minimizing false negatives), but may have reduced precision; DNNs can maximize accuracy at cost of recall (Chadha, 2024). Ensemble strategies can boost recall above 90% while preserving specificity (Akib et al., 1 Dec 2025, Islam et al., 18 May 2025).
• Comorbid prediction: In specialized settings (ICU/postoperative), models using selected comorbidity variables and scores—e.g., Charlson Comorbidity Index, CKD, heart failure—can outperform models using CCI alone, as evidenced by SVM and CatBoost outperforming CCI-only references (AUC SVM = 0.855 [0.829–0.878]) (Pan et al., 15 Mar 2025).
• Imaging models: MRI/CT-based methods relying on deep feature extraction and advanced ensemble classifiers (e.g., MobileNetV2+LDA+SVC for CT, AE²-LSTM for multimodal MRI) achieve 70–97% accuracy depending on cohort and setup (Hossen et al., 4 Jul 2025, Hatami et al., 2023).

A representative performance table excerpt (held-out test set, after balancing) (Tashkova et al., 14 May 2025):

Model	Accuracy	Sensitivity	Specificity	F1-score	ROC AUC
Logistic Reg.	85%	82%	88%	81%	0.88
Random Forest	94%	94%	94%	94%	0.97
XGBoost	93%	92%	94%	92%	0.95

5. Interpretability, Feature Importance, and Model Explanation

Interpretability is addressed through a combination of inherent model structure (linear/logistic, decision trees, Bayesian Rule Lists), post-hoc attribution (SHAP, LIME), and feature importance metrics (Gini, permutation, point-biserial correlation):

• Tree ensembles: Gini impurity and permutation importance map feature relevance, consistently highlighting age, hypertension, average glucose, BMI, and comorbidity indices (Ismail et al., 2023, Tashkova et al., 14 May 2025, Akib et al., 1 Dec 2025).
• SHAP/LIME: Additive value decompositions elucidate both global rankings and per-patient attributions; age, hypertension, and glucose typically receive the largest mean absolute weights (Akib et al., 1 Dec 2025, Li et al., 2 Jun 2025). In ICU/postoperative models, variables such as previous cerebrovascular disease, serum creatinine, and SBP are leading predictors by SHAP drop analysis (Li et al., 2 Jun 2025).
• Rule-based models: Sparse BRL classifiers are as interpretable as CHADS₂ scoring, with explicit risk intervals and logical structure (Letham et al., 2015).
• Imaging-based: Integrated gradients, attention maps, or visual saliency are sometimes applied for slice-wise MRI/CT rationale, though more often called out as future work (Hatami et al., 2023, Hossen et al., 4 Jul 2025).

Clinically actionable explanations are essential for trust and integration into electronic health records or bedside support systems.

6. Advancements, Clinical Implementation, and Limitations

Advancements in stroke prediction modeling include:

• Dynamic causal inference: VAR+GNN combined workflows extract lagged and nonlinear relationships from longitudinal health data (e.g., CHARLS), improving static model AUC by 2–5% (Zheng et al., 10 Mar 2025).
• Hybrid ensemble pipelines: Stacking, voting, and probability fusion (RF+ET+XGB, base+meta-learners) achieve near-perfect discrimination in balanced-cohort settings, but may require calibration/validation to avoid overfitting (Islam et al., 18 May 2025, Akib et al., 1 Dec 2025, Zhiwan et al., 17 Apr 2025).
• Imaging integration: Pre-trained deep CNNs combined with supervised LDA, feature selection (BFO/PCA), and SVC classifiers drive imaging stroke diagnosis to >97% accuracy (Hossen et al., 4 Jul 2025).
• Clinical utility: Real-time risk estimation tools (EHR embedding, alert systems) and personalized prevention pipelines are feasible as model interpretability and discrimination increase (Akib et al., 1 Dec 2025, Li et al., 2 Jun 2025).

Limitations persist:

• Data heterogeneity: Model performance is sensitive to source cohort, population structure, and predictor selection. External and prospective validation are necessary to ensure generalizability (Fernandez-Lozano et al., 2024, Pan et al., 15 Mar 2025).
• Imbalance, overfitting: High performance on ROS/SMOTE-balanced data may not reflect clinical deployment settings where event rates are low (Akib et al., 1 Dec 2025).
• Black-box risk: Many DNN-based or ensemble models lack visibility without explicit SHAP/LIME analysis, which is a barrier in regulatory or clinical adoption contexts.
• Sample size: Many imaging and hybrid models are validated only on small institutional cohorts; scaling to multicenter or population-based settings remains open (Hatami et al., 2023, Hossen et al., 4 Jul 2025).

7. Future Directions and Theoretical Implications

Current research trajectories suggest:

• Temporal modeling and survival analysis: Time-to-event architectures (Cox, Cox-Deep, RNNs with temporal health data) for dynamic risk estimation and targetable intervention scheduling (Dai et al., 2023, Zheng et al., 10 Mar 2025).
• Multimodal data fusion: Integration of genomics, proteomics, and advanced imaging data for holistic risk assessment.
• Personalized and stratified models: Age-, sex-, or risk-stratified learners can enhance accuracy for specific subgroups, mitigating the "one-size-fits-all" limitation of global models (Tashkova et al., 14 May 2025).
• Automated deployment: Integration with clinical workflows (EHR, mobile-device platforms) for real-time risk monitoring and clinical decision-support with interpretable alerts (Akib et al., 1 Dec 2025, Zhiwan et al., 17 Apr 2025).
• Explainable deep learning: Efforts to transparently expose predictions of complex models (gradient-based interpretation, attention mechanisms) remain critical for regulatory acceptance and clinician adoption (Akib et al., 1 Dec 2025, Hossen et al., 4 Jul 2025).

Validation on diverse, multicenter cohorts, continuous updating with new data, and careful calibration will be necessary for responsible and scalable real-world implementation.