Machine learning to predict metabolic-associated fatty liver disease

Table 1 Overview of machine learning algorithms used by Tian et al[1]

Algorithm	Main strengths	Limitations/considerations	Role in Tian et al’s study[1]
LR	Simple, interpretable, baseline comparator	Limited handling of non-linear relationships	Served as reference model
RF	Robust to overfitting, good for tabular data	Less interpretable, may require tuning	Moderate performance
SVM	Effective with high-dimensional data	Sensitive to parameter choice, less scalable	Tested but lower accuracy
XGBoost	Handles non-linear interactions, high accuracy, efficient	“Black box” risk, requires interpretability tools	Best-performing model (AUC = 0.82; CV AUC = 0.918)

LR: Logistic regression; RF: Random forest; SVM: Support vector machine; AUC: Area under the curve; CV: Cross-validation.

Table 2 Key methodological features of the study by Tian et al[1]

Methodological aspect	Approach used	Rationale/significance
Reference standard for steatosis	FibroScan® (CAP ≥ 238 dB/m)	More accurate and reproducible than conventional ultrasonography; strengthens diagnostic validity
Class imbalance	SMOTE	Mitigates underrepresentation of non-MAFLD cases; reduces bias in training
Feature selection	Recursive feature elimination + LASSO regression	Reduced 156 variables to 10 key predictors with strong pathophysiological relevance
TCM feature acquisition	ICI + expert review	Attempt to standardize and objectify TCM-derived indicators
Model interpretability	SHAP analysis	Quantified relative contribution of each predictor; improved clinical interpretability

CAP: Controlled attenuation parameter; SMOTE: Synthetic minority over-sampling technique; MAFLD: Metabolic-associated fatty liver disease; LASSO: Least absolute shrinkage and selection operator; TCM: Traditional Chinese medicine; ICI: Intelligent Constitution Identifier; SHAP: SHapley Additive exPlanation.