Machine learning-based identification of biochemical markers to predict hepatic steatosis in patients at high metabolic risk

Table 3 Performance comparison of each model

Models	Accuracy	Precision	Recall	F1-score
Random forest	0.83 (0.7663-0.8804)	0.82 (0.7518-0.8803)	0.83 (0.7717-0.8804)	0.81 (0.7439-0.8692)
XGBoost	0.84 (0.7989-0.9076)	0.82 (0.7987-0.9119)	0.82 (0.7820-0.8533)	0.84 (0.7741-0.8450)
Logistic regression	0.78 (0.7174-0.8370)	0.78 (0.7233-0.8379)	0.78 (0.7120-0.8370)	0.78 (0.7143-0.8340)
Support vector machine	0.78 (0.7227-0.8424)	0.77 (0.7042-0.8372)	0.78 (0.7174-0.8424)	0.78 (0.7100-0.8352)