Cross-cohort Generalization for Heart Disease Prediction with Explainable AI.

Authors

Prosper Ughakpoteni, Yaseen Akhtar, Ahmad Chaddad, Sarah Alkhodair, Tareef Daqqaq

Published in

Journal of cardiovascular translational research. Volume 19. Issue 1. Sep 23, 2026. Epub Sep 23, 2026.

Abstract

We propose CardioTransfer-X, a cross-cohort transfer learning framework within a related clinical benchmark family for tabular CVD risk prediction that yields performance comparable to training from scratch while preserving transparency. Predictive models are pre-trained on a composite multi-hospital heart disease dataset to learn generalized risk patterns, then fine-tuned on the smaller, distributionally distinct Cleveland Heart Disease dataset. To mitigate dataset shift and class imbalance, we introduce a stability-aware, correlation-based feature selection strategy and apply SMOTE only during training. CardioTransfer-X is instantiated with XGBoost, TabNet, and a MLP, enabling systematic comparison across tree-based and neural models. SHAP-based explanations provide global interpretability across architectures, with consistent feature importance rankings before and after fine-tuning. Fine-tuned XGBoost achieves 80.65±4.08% accuracy and 81.74±3.71% F1-score on the Cleveland dataset while maintaining stable source-domain performance, showing performance competitive with training from scratch, while shifting the decision boundary toward higher sensitivity and maintaining clinically meaningful feature attributions.

PMID:
42776352
Bibliographic data and abstract were imported from PubMed on 24 Sep 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.