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.