TABLE 2.

Using ML Algorithms to Identify Patients at High Risk for NAFLD and Determine the Severity of the Disease

Study	No. of Patients	Purpose	Gold Standard	Algorithms Used	Key Variables	AUROC or Accuracy (%)	Sensitivity	Specificity
Yip et al. 1	922	Patient identification using EMR	MRI	Logistic regression Ridge regression* Decision tree AdaBoost	ALT High‐density lipoprotein cholesterol TGs HbA1c White blood cells Hypertension	AUROC: 0.88	0.923	0.904
Ma et al. 2	10,508	Patient identification using EMR	Ultrasound	Logistic regression* κ‐nearest neighbor Support vector machines* Naive Bayes Bayes network* Decision tree AdaBoost Bootstrap aggregating Random forest AODEs*	Age Sex BMI ALT AST Alkaline phosphatase GGT TGs Blood urea nitrogen Bilirubin Cholesterol Creatinine Fasting glucose Uric acid	Accuracy: 83%	0.68	0.946
Sowa et al. 5	126	Determining severity of fibrosis	Liver biopsy	Logistic regression κ‐nearest neighbor Support vector machines Rule based Decision tree Random forest*	ALT AST M30 M65 HA	AUROC: 0.67	0.60	0.77
						Accuracy: 79%
Canbay et al. 6	164	Determine histological severity	Liver biopsy	Ensemble feature selection* Logistic regression*	Age Sex BMI ALT AST M30 M65 HbA1c Fasting glucose Cholesterol Adiponectin	AUROC: 0.73

^*Identifies most significant ML algorithms used in data analyses.