Machine learning-based models for advanced fibrosis in non-alcoholic steatohepatitis patients: A cohort study

Table 1 Basic clinical characteristics of non-alcoholic steatohepatitis patients

Variables	Total (n = 746)	Validation cohort (n = 224)	Training cohort (n = 522)	P value
Age (years)	44.45 ± 15.11	44.03 ± 15.51	44.62 ± 14.95	0.623
Gender				0.328
Female	416 (55.76)	131 (58.48)	285 (54.60)
Male	330 (44.24)	93 (41.52)	237 (45.40)
Hypertension	352 (47.18)	101 (45.09)	251 (48.08)	0.453
Diabetes	345 (46.25)	97 (43.30)	248 (47.51)	0.291
Smoking	137 (18.36)	35 (15.62)	102 (19.54)	0.206
BMI	28.16 ± 4.70	27.82 ± 4.84	28.31 ± 4.63	0.186
ALT	101.39 ± 127.91	90.11 ± 106.34	106.22 ± 135.93	0.115
AST	59.42 ± 77.85	50.85 ± 51.24	63.09 ± 86.59	0.017
Total bilirubin	16.08 ± 18.76	14.76 ± 17.61	16.64 ± 19.22	0.21
Albumin	44.04 ± 4.60	43.86 ± 4.68	44.12 ± 4.57	0.474
γ-GGT	94.74 ± 125.55	85.23 ± 117.60	98.90 ± 128.77	0.183
Creatinine	4.78 ± 1.26	4.77 ± 1.31	4.78 ± 1.25	0.902
Blood urea nitrogen	65.03 ± 14.65	64.49 ± 14.71	65.27 ± 14.64	0.51
Leukocyte	6.35 ± 1.81	6.25 ± 1.78	6.40 ± 1.83	0.317
Platelet	224.33 ± 71.99	227.77 ± 78.80	222.87 ± 68.92	0.422
INR	2.23 ± 9.44	3.22 ± 13.71	1.79 ± 6.77	0.059
Total cholesterol	4.77 ± 0.97	4.78 ± 0.98	4.76 ± 0.96	0.74
Triglyceride	2.23 ± 1.26	2.28 ± 1.33	2.21 ± 1.23	0.477
Complement C3	1.10 ± 0.34	1.09 ± 0.35	1.10 ± 0.33	0.704
Complement C4	0.23 ± 0.08	0.21 ± 0.06	0.23 ± 0.09	0.015
HbA1c	7.28 ± 2.58	7.16 ± 2.52	7.34 ± 2.60	0.504
VCTE-CAP	296.03 ± 46.89	299.15 ± 49.03	294.80 ± 46.04	0.385
VCTE-LSM	9.02 ± 5.05	9.56 ± 6.81	8.81 ± 4.15	0.166
APRI	0.33 ± 0.61	0.29 ± 0.40	0.35 ± 0.69	0.218
FIB-4	241.40 ± 915.28	299.15 ± 49.03	227.76 ± 1017.03	0.437
F3/F4	278 (37.27)	81 (36.16)	197 (37.74)	0.683

ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; γ-GGT: Gamma-glutamyl transferase; INR: International normalized ratio; APRI: Aspartate aminotransferase to platelet ratio index; FIB-4: Fibrosis index based on the 4 factors; BMI: Body mass index; HbA1c: Glycated hemoglobin; LSM: Liver stiffness measurement.

Table 2 Diagnostic performance of the machine learning models for moderate to advanced liver fibrosis

Cohort	Models	Accuracy	Area under receiver operating characteristic curve	F1 scores	Sensitivity	Specificity	Positive predictive value	Negative predictive value	P value
Training	XGB	0.862 (0.830-0.891)	0.934 (0.914-0.955)	0.838	0.958	0.744	0.856	0.876
	RF	0.877 (0.846-0.904)	0.857 (0.825-0.888)	0.817	0.944	0.72	0.869	0.894	< 0.001
	SVM	0.8199 (0.784-0.852)	0.773 (0.738-0.809)	0.724	0.969	0.578	0.789	0.92	< 0.001
	LR	0.784 (0.746-0.818)	0.805 (0.764-0.846)	0.715	0.901	0.593	0.782	0.787	< 0.001
	NB	0.385 (0.343-0.428)	0.503 (0.499-0.575)	0.012	0.006	1	1	0.383	< 0.001
Validation	XGB	0.853 (0.799-0.896)	0.917 (0.880-0.953)	0.78	0.959	0.658	0.837	0.897
	RF	0.875 (0.824-0.915)	0.840 (0.788-0.892)	0.758	0.959	0.626	0.863	0.905	< 0.001
	SVM	0.813 (0.755-0.861)	0.740 (0.684-0.796)	0.658	0.986	0.494	0.784	0.951	< 0.001
	LR	0.790 (0.731-0.842)	0.745 (0.669-0.821)	0.641	0.959	0.481	0.772	0.864	< 0.001
	NB	0.357 (0.294-0.424)	0.503 (0.495-0.510)	0.014	0.007	1	1	0.354	< 0.001

LR: Logistic regression; RF: Random forest; SVM: Support Vector Machine; NB: Naive Bayes; XGBoost: Extreme Gradient Boosting.

Table 3 Diagnostic performance of the Extreme Gradient Boosting model and other non-invasive diagnosis models for advanced liver fibrosis

Cohort	Models	Accuracy	Area under receiver operating characteristic curve	Sensitivity	Specificity	Positive predictive value	Negative predictive value	P value
Training	XGB	0.810 (0.764-0.841)	0.934 (0.914-0.955)	0.958	0.744	0.786	0.914
	XGB + LSM	0.844 (0.7815-0.898)	0.977 (0.966-0.980)	0.985	0.758	0.837	0.929	< 0.001
	APRI	0.715 (0.674-0.753)	0.803 (0.765-0.841)	0.907	0.402	0.711	0.727	< 0.001
	FIB-4	0.734 (0.774-0.848)	0.811 (0.774-0.848)	0.898	0.467	0.732	0.738	< 0.001
Validation	XGB	0.794 (0.765-0.862)	0.917 (0.880-0.953)	0.925	0.658	0.756	0.884
	XGB + LSM	0.821 (0.755-0.880)	0.970 (0.950-0.990)	0.954	0.739	0.81	0.908	< 0.001
	APRI	0.710 (0.646-0.769)	0.737 (0.669-0.805)	0.917	0.329	0.715	0.684	< 0.001
	FIB-4	0.688 (0.622-0.748)	0.752 (0.687-0.816)	0.912	0.165	0.681	0.765	< 0.001

LSM: Liver stiffness measurement; APRI: Aspartate aminotransferase to platelet ratio index; FIB-4: Fibrosis index based on the 4 factors; XGBoost: Extreme Gradient Boosting.