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Updated: Jan 11, 2026
Mouse Model of Metabolic Dysfunction-Associated Steatotic Liver Disease with Fibrosis
Published on: July 18, 2025
Explainable machine learning model for classifying atherosclerotic cardiovascular disease in patients with metabolic
Zhengliang Li1, Xiaokai Chen2, Linlin Ren2
1Department of Cardiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Insights
Machine learning models accurately predict atherosclerotic cardiovascular disease (ASCVD) risk in patients with metabolic dysfunction-associated steatotic liver disease (MASLD). These models, particularly Gradient Boosting, offer improved early risk stratification for ASCVD in MASLD.
Area of Science:
- Cardiology
- Hepatology
- Medical Informatics
Background:
- Metabolic dysfunction-associated steatotic liver disease (MASLD) significantly increases cardiovascular disease (CVD) mortality risk.
- Traditional CVD risk predictors show limitations in MASLD patient populations.
- Accurate ASCVD risk assessment is crucial for MASLD management.
Purpose of the Study:
- Develop and validate machine learning (ML) models to classify prevalent atherosclerotic cardiovascular disease (ASCVD) risk in MASLD patients.
- Enhance the interpretability of ML models using SHapley Additive exPlanations (SHAP) for clinical application.
- Identify key predictive features for ASCVD in MASLD.
Main Methods:
- Retrospective analysis of 590 MASLD patients.
- Development of six ML models, including Gradient Boosting (GB), using LASSO regression for feature selection.
- Model performance evaluated by AUC, accuracy, sensitivity, specificity, and F1 score.
- SHAP analysis for feature importance interpretation.
Main Results:
- The Gradient Boosting (GB) model demonstrated high performance with AUCs of 0.918 (training) and 0.817 (validation).
- Key predictors identified by SHAP include the Cholesterol-HDL-Glucose (CHG) index, Castelli Risk Index II (CRI-II), lipoprotein(a) [Lp(a)], serum creatinine (Scr), and uric acid (UA).
- ASCVD was prevalent in 73.6% of the study cohort.
Conclusions:
- The developed GB model accurately identifies existing ASCVD in MASLD patients.
- This ML model shows potential as a valuable tool for early ASCVD risk stratification in clinical practice.
- SHAP analysis provides insights into the drivers of ASCVD risk in MASLD.
Background:
Cardiovascular disease (CVD) is the leading cause of mortality in patients with metabolic dysfunction-associated steatotic liver disease (MASLD), yet traditional risk predictors remain limited in clinical practice.
Objective:
To develop machine learning (ML) models for classifying prevalent atherosclerotic cardiovascular disease (ASCVD) risk in MASLD patients, and to enhance model interpretability using SHapley Additive exPlanations (SHAP). Methods: This retrospective study included 590 MASLD patients diagnosed at the Affiliated Hospital of Qingdao University between December 2019 and December 2024. Patients were randomly divided into a training set (n=413) and a validation set (n=177), and further stratified based on ASCVD status. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection. Six ML models were developed and evaluated using sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC), and F1 score. SHAP analysis was performed to interpret feature contributions.
Results:
ASCVD was present in 434 of 590 patients (73.6%). The Gradient Boosting (GB) model achieved the best performance, with AUCs of 0.918 (95% CI: 0.890-0.944) in the training set and 0.817 (95% CI: 0.739-0.883) in the validation set. SHAP analysis identified the top predictors as the Cholesterol-HDL-Glucose (CHG) index, Castelli Risk Index II (CRI-II), lipoprotein(a) [Lp(a)], serum creatinine (Scr), and uric acid (UA).
Conclusion:
The GB model demonstrated strong high accuracy in identifying existing ASCVD in MASLD patients and may serve as a useful tool for early risk stratification in clinical settings.
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