Development and Validation of a Machine Learning–Based Prediction Model for Cardiovascular Disease in Patients with Metabolic Dysfunction–Associated Fatty Liver Disease

Background/Aim: Metabolic dysfunction–associated fatty liver disease (MAFLD) is strongly associated with increased cardiovascular disease (CVD) risk. However, traditional cardiovascular risk assessment tools may inadequately capture the complex pathophysiology linking hepatic and CVD in MAFLD patients. This study aimed to develop and validate machine learning models to predict CVD risk in MAFLD patients.

Materials and Methods: This cross-sectional study analyzed NHANES 2017-2023 data, with participants randomly split into training (70%) and validation (30%) sets.

Results: A total of 6828 adults with MAFLD were included (13.1% with prevalent CVD). Least Absolute Shrinkage and Selection Operator regression identified 14 key predictors, with age, nonalcoholic fatty liver disease Fibrosis Score (NFS), and albumin emerging as the most influential. Critically, liver-specific markers (NFS: mean |SHAP| = 0.0299; albumin: 0.0289) demonstrated superior predictive capacity compared to traditional cardiovascular risk factors (hypertension: 0.0145; diabetes: 0.0107; smoking: 0.0035). SHapley Additive exPlanations analysis revealed that older age, higher NFS (particularly > −1.0), and lower albumin (<3.5 g/dL) were the strongest drivers of CVD risk, with NFS showing clear threshold effects.

Conclusion: The findings confirm that traditional cardiovascular risk assessment approaches are insufficient for MAFLD patients, as liver-specific markers—particularly hepatic fibrosis (NFS) and liver synthetic function (albumin)—dominated cardiovascular risk prediction over conventional risk factors (hypertension, diabetes, smoking). This paradigm shift underscores the necessity of integrated liver-heart assessment in MAFLD and supports routine hepatic fibrosis evaluation for cardiovascular risk stratification. The model demonstrates immediate clinical applicability through existing electronic health records, enabling early identification of high-risk patients for targeted preventive interventions.

Cite this article as: Wang, L.-H. Development and validation of a machine learning–based prediction model for cardiovascular disease in patients with metabolic dysfunction–associated fatty liver disease. Turk J Gastroenterol. 2026;37(4):471-482.