Hepatic fibrosis poses a significant health risk for young adults with type 2 diabetes (T2D). We propose FCFNets, a novel factual and counterfactual learning framework to predict hepatic fibrosis in young adults with T2D that can address class imbalance issue and increase interpretability leveraging electronic health records (EHRs). We designed a hybrid UNDO oversampling strategy, combining random and dissimilar oversampling that improves dataset diversity and model robustness. FCFNets also integrates SHAP-based global and instance-level explanations, alongside feature interaction analysis, providing insights into critical risk factors associated with hepatic fibrosis. The results show our proposed model outperforms various baseline methods with high sensitivity (0.846) and accuracy (0.768), while delivering counterfactual explanations. Hyperparameter tuning and dropout analysis further refine the model, ensuring optimal performance. This study demonstrates FCFNets potential for early detection and personalized management of hepatic fibrosis, paving the way for interpretable AI applications in precision medicine.

The authors have declared no competing interest.

This study did not receive any funding

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The study has been approved by the University of Florida Institutional Review Board (protocol no. IRB202300939)

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