Machine learning approaches using blood biomarkers in non-alcoholic fatty liver diseases

Table 1 Blood biomarker panels for nonalcoholic fatty liver disease and nonalcoholic steatohepatitis

Blood biomarker panels for steatosis
Panel	Patient	Anthropometry	Blood biomarkers
FLI	-	BMI, Waist circumference	GGT and TG
HSI	Presence of DM	BMI	AST:ASL
Steatotest	Sex	BMI	ALT, GGT, TG, A2M, ApoA1, haptoglobin, bilirubin,cholesterol, and glucose
LAP	Sex	Waist circumference	TG
ION	Sex	Waist to hip ratio	ALT, TG
NAFLD LFS	Presence of DM and MS	-	AST:ALT, Insulin
Blood biomarker panels for fibrosis
Panel	Patient	Anthropometry	Blood biomarkers
APRI	-	-	Platelet count, AST
FIB-4	Age	-	Platelet count, AST, ALT
FibroTest	Age, sex	BMI	GGT, A2M, ApoA1, haptoglobin, and total bilirubin
Fibrometer	Age	Body weight	Platelet count, AST, ALT, glucose, ferritin
ELF	-	-	Hyaluronic acid, PIIINP and TIMP-1
Hepascore	Age, sex	-	GGT, Hyaluronic acid, PIIINP and TIMP-1
BARD	Presence of DM	BMI	AST:ALT
NFS	Age, sex, Presence of DM	-	Platelet count, AST:ALT, Albumin

A2M: Alpha-2-macroglobulin; ALT: alanine aminotransferase; ApoA1: Apolipoprotein A1; AST: Aspartate aminotransferase; BMI: body mass index; DM: Diabetes mellitus; GGT: gamma-glutamyl transpeptidase; MS: Metabolic syndrome; NAFLD: Nonalcoholic fatty liver disease; PIIINP: Amino-terminal propeptide of type III procollagen; TG: Triglycerides; TIMP1: tissue inhibitor of matrix metalloproteinases-1.

Table 2 Machine learning studies in nonalcoholic fatty liver disease and nonalcoholic steatohepatitis patients

Ref.	Patients	Investigated biomarker	Model with best performance	Results
Sowa et al[32], 2013	126 patients	Alanine aminotransferase; Aspartate aminotransferase; M30; M60; Hyaluronic acid	Randon forest	79% Accuracy in fibrosis prediction; 60% sensitivity; 77% specificity
Yip et al[33], 2017	922 patients	Alanine aminotransferase; High-density lipoprotein cholesterol; Triglycerides; HbA1c; White blood cells; Hypertension	Ridge score	88% Accuracy in steatosis prediction; 92% sensitivity; 90% specificity
Ma et al[34], 2018	10.508 patients; 2522 NAFLD patients	Age; Sex; Body mass index; Alanine aminotransferase; Aspartate aminotransferase; Alkaline phosphatase; Gamma-glutamyl transpeptidase; Triglycerides; Blood urea nitrogen; Bilirubin; Cholesterol; Creatinine; Fasting glucose; Uric acid	Bayesian network model	83% Accuracy in NAFLD prediction; 68% sensitivity; 94% specificity
Canbay et al[35], 2019	164 patients; 122 (validation)	Age; HbA1c; Gamma-glutamyl transpeptidase; M30; Adiponectin	Logistic regression	70% Accuracy in separate NAFLD and NASH
Liu et al[36], 2021	15.315 patients5878 with NAFLD	Body mass index; Waist circumference; Waist-to-height ratio; Alanine aminotransferase; Fasting blood glucose; Gamma-glutamyl transpeptidase; Very-low-density lipoprotein cholesterol; Low-density lipoprotein cholesterol; High-density lipoprotein cholesterol; Systolic blood pressure; Alkaline phosphatase; Diastolic blood pressure	XGBoost model	79% Accuracy in NAFLD prediction; 61% sensitivity; 90% specificity
Pei et al[37], 2021	3.419 patients; 845 with fat liver diseases	Age; Height; Hemoglobin; Aspartate aminotransferase; Glucose; Uric acid; Low-density lipoprotein; Alpha-fetoprotein; Triglycerides; High-density lipoprotein; Carcinoembryonic antigen	XGBoost model	94% accuracy of prediction; 90% sensitivity; 95% specificity

NAFLD: Nonalcoholic fatty liver disease; XGBoost: Extreme gradient boosting.