Journal of Southern Medical University ›› 2026, Vol. 46 ›› Issue (8): 1861-1869.doi: 10.12122/j.issn.1673-4254.2026.08.13
Development and validation of a machine learning-based prediction model for fatty liver in Wilson disease
Shiheng SHI1(
), Daiping HUA1, Shang XIANG1, Lanting SUN1, Qiaoyu XUAN1, Wenming YANG1,2, Han WANG1,2()
- 1.Department of Neurology, First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei 230031, China
2.Key Laboratory of Xin'an Medicine, Ministry of Education, Anhui University of Chinese Medicine, Hefei 230038, China
-
Received:2025-12-22Online:2026-08-20Published:2026-08-01 -
Contact:Han WANG E-mail:156767269@qq.com;neuwhah@126.com -
Supported by:Regional Innovation and Development Joint Fund of National Natural Science Foundation of China(U22A20366)
Cite this article
Shiheng SHI, Daiping HUA, Shang XIANG, Lanting SUN, Qiaoyu XUAN, Wenming YANG, Han WANG. Development and validation of a machine learning-based prediction model for fatty liver in Wilson disease[J]. Journal of Southern Medical University, 2026, 46(8): 1861-1869.
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URL: https://www.j-smu.com/EN/10.12122/j.issn.1673-4254.2026.08.13
Fig.1 Flowchart of the development and validation of the fatty liver prediction model in WD patients.
Fig.1 Flowchart of the development and validation of the fatty liver prediction model in WD patients.
Tab.1 Baseline characteristics of WD patients with and without fatty liver ［Median (IQR)］
| Variable | Overall (n=1862) | Non-fatty liver group (n=566) | Fatty liver group (n=1296) | P |
|---|---|---|---|---|
| Male[n (%)] | 1057 (56.77) | 312 (55.12) | 745 (57.48) | 0.371 |
| Age (years) | 30.00 (23.00, 37.00) | 29.00 (23.00, 36.00) | 30.00 (23.00, 37.00) | 0.170 |
| BMI (kg/m2) | 21.26 (19.05, 23.79) | 21.22 (19.16, 23.61) | 21.30 (19.05, 23.92) | 0.600 |
| WBC (×10⁹/L) | 4.60 (3.61, 5.83) | 4.72 (3.84, 5.96) | 4.52 (3.51, 5.73) | 0.003 |
| RBC (×10¹²/L) | 4.37 (3.99, 4.79) | 4.39 (4.07, 4.78) | 4.36 (3.95, 4.79) | 0.038 |
| Hb (g/L) | 130.00 (118.00, 142.00) | 130.00 (119.00, 142.00) | 130.00 (117.00, 142.00) | 0.377 |
| PLT (×10⁹/L) | 144.00 (94.00, 203.00) | 159.50(111.00, 217.00) | 138.00 (87.75, 196.25) | <0.001 |
| RDW (%) | 13.30 (12.70, 14.30) | 13.10 (12.50, 13.90) | 13.40 (12.70, 14.50) | <0.001 |
| ALT (U/L) | 30.40 (20.02, 53.10) | 24.10 (17.00, 40.30) | 34.00 (21.98, 59.12) | <0.001 |
| AST (U/L) | 29.00 (22.00, 43.60) | 26.00 (20.40, 36.00) | 30.65(23.00, 46.00) | <0.001 |
| TP (g/L) | 63.60 (60.00, 67.60) | 64.10 (60.60, 67.77) | 63.40 (59.80, 67.43) | 0.048 |
| GLB (g/L) | 24.40 (21.90, 27.70) | 24.20 (21.90, 27.08) | 24.50 (21.90, 27.90) | 0.141 |
| A/G | 1.61 (1.36, 1.86) | 1.64 (1.41, 1.87) | 1.59 (1.34, 1.86) | 0.014 |
| GGT(U/L) | 31.00 (19.00, 58.00) | 28.00 (18.00, 52.00) | 32.00 (20.00, 61.25) | <0.001 |
| ALP (U/L) | 96.00 (76.00, 124.00) | 93.00 (75.00, 118.00) | 98.00 (78.00, 127.00) | 0.002 |
| LDH (U/L) | 168.00 (146.00, 198.00) | 166.00 (145.25, 192.00) | 170.00 (146.75, 201.25) | 0.010 |
| GLU (mmol/L) | 4.61 (4.32, 4.93) | 4.62 (4.34, 4.95) | 4.60 (4.31, 4.92) | 0.454 |
| TG (mmol/L) | 0.92 (0.71, 1.22) | 0.97 (0.75, 1.29) | 0.88 (0.69, 1.19) | <0.001 |
| TC (mmol/L) | 4.05 (3.51, 4.66) | 4.08 (3.61, 4.66) | 4.04 (3.47, 4.65) | 0.131 |
| HDL-C (mmol/L) | 1.26 (1.07, 1.48) | 1.27 (1.08, 1.52) | 1.26 (1.06, 1.47) | 0.008 |
| LDL-C (mmol/L) | 2.38 (2.01, 2.82) | 2.38 (2.01, 2.79) | 2.38 (2.00, 2.83) | 0.748 |
| TG/HDL-C | 0.72 (0.52, 1.02) | 0.74 (0.53, 1.02) | 0.70 (0.51, 1.02) | 0.349 |
| TBA (μmol/L) | 7.80 (4.50, 16.20) | 5.80 (3.70, 9.70) | 9.15 (5.00, 21.92) | <0.001 |
| Lp(a) (mg/L) | 53.75 (26.70, 115.50) | 58.45 (27.50, 129.10) | 51.65 (26.17, 110.93) | 0.079 |
| Hcy (μmol/L) | 11.30 (8.30, 15.80) | 11.10 (8.20, 15.88) | 11.30 (8.30, 15.72) | 0.718 |
| UA (μmol/L) | 222.50 (173.25, 285.75) | 221.50 (174.00, 282.00) | 223.00 (172.75, 286.00) | 0.860 |
| BUN (mmol/) | 4.88 (3.99, 5.90) | 4.93 (4.11, 5.98) | 4.87 (3.95, 5.85) | 0.076 |
| Cr (μmol/L) | 65.20 (54.00, 79.18) | 65.55 (53.12, 82.07) | 64.95 (54.68, 78.23) | 0.541 |
| PⅢNP (ng/mL) | 14.24 (9.92, 23.26) | 12.37 (9.18, 19.11) | 15.21 (10.20, 24.79) | <0.001 |
| LN (ng/mL) | 104.36 (80.90, 140.61) | 98.98 (76.04, 126.21) | 108.03 (82.45, 151.14) | <0.001 |
| HA (ng/mL) | 123.04 (71.61, 226.08) | 102.69 (61.83, 176.16) | 134.66 (77.42, 248.96) | <0.001 |
| CIV (ng/mL) | 66.87 (49.04, 101.60) | 57.42 (40.35, 79.25) | 72.22 (51.76, 109.73) | <0.001 |
| INR | 1.01 (0.95, 1.09) | 1.00 (0.94, 1.07) | 1.02 (0.96, 1.11) | <0.001 |
| FIB (g/L) | 2.08 (1.78, 2.42) | 2.17 (1.86, 2.54) | 2.04 (1.74, 2.38) | <0.001 |
| PT (s) | 31.00 (29.10, 33.10) | 30.70 (28.92, 32.68) | 31.00 (29.10, 33.40) | 0.002 |
| APTT (s) | 11.50 (10.80, 12.40) | 11.30 (10.60, 12.00) | 11.50 (10.90, 12.60) | <0.001 |
| TBIL (μmol/L) | 15.20 (11.30, 20.58) | 13.60 (9.10, 17.98) | 15.91 (12.00, 21.70) | <0.001 |
| DBIL (μmol/L) | 3.20 (2.40, 4.50) | 2.80 (1.90, 3.90) | 3.40 (2.50, 4.73) | <0.001 |
| IBIL (μmol/L) | 11.80 (8.80, 16.00) | 10.30 (7.00, 13.90) | 12.40 (9.47, 17.00) | <0.001 |
| 24-h UCu (μg/24 h) | 748.37 (423.13, 1151.57) | 703.16 (420.82, 1108.14) | 762.30 (427.12, 1162.81) | 0.294 |
Tab.1 Baseline characteristics of WD patients with and without fatty liver ［Median (IQR)］
| Variable | Overall (n=1862) | Non-fatty liver group (n=566) | Fatty liver group (n=1296) | P |
|---|---|---|---|---|
| Male[n (%)] | 1057 (56.77) | 312 (55.12) | 745 (57.48) | 0.371 |
| Age (years) | 30.00 (23.00, 37.00) | 29.00 (23.00, 36.00) | 30.00 (23.00, 37.00) | 0.170 |
| BMI (kg/m2) | 21.26 (19.05, 23.79) | 21.22 (19.16, 23.61) | 21.30 (19.05, 23.92) | 0.600 |
| WBC (×10⁹/L) | 4.60 (3.61, 5.83) | 4.72 (3.84, 5.96) | 4.52 (3.51, 5.73) | 0.003 |
| RBC (×10¹²/L) | 4.37 (3.99, 4.79) | 4.39 (4.07, 4.78) | 4.36 (3.95, 4.79) | 0.038 |
| Hb (g/L) | 130.00 (118.00, 142.00) | 130.00 (119.00, 142.00) | 130.00 (117.00, 142.00) | 0.377 |
| PLT (×10⁹/L) | 144.00 (94.00, 203.00) | 159.50(111.00, 217.00) | 138.00 (87.75, 196.25) | <0.001 |
| RDW (%) | 13.30 (12.70, 14.30) | 13.10 (12.50, 13.90) | 13.40 (12.70, 14.50) | <0.001 |
| ALT (U/L) | 30.40 (20.02, 53.10) | 24.10 (17.00, 40.30) | 34.00 (21.98, 59.12) | <0.001 |
| AST (U/L) | 29.00 (22.00, 43.60) | 26.00 (20.40, 36.00) | 30.65(23.00, 46.00) | <0.001 |
| TP (g/L) | 63.60 (60.00, 67.60) | 64.10 (60.60, 67.77) | 63.40 (59.80, 67.43) | 0.048 |
| GLB (g/L) | 24.40 (21.90, 27.70) | 24.20 (21.90, 27.08) | 24.50 (21.90, 27.90) | 0.141 |
| A/G | 1.61 (1.36, 1.86) | 1.64 (1.41, 1.87) | 1.59 (1.34, 1.86) | 0.014 |
| GGT(U/L) | 31.00 (19.00, 58.00) | 28.00 (18.00, 52.00) | 32.00 (20.00, 61.25) | <0.001 |
| ALP (U/L) | 96.00 (76.00, 124.00) | 93.00 (75.00, 118.00) | 98.00 (78.00, 127.00) | 0.002 |
| LDH (U/L) | 168.00 (146.00, 198.00) | 166.00 (145.25, 192.00) | 170.00 (146.75, 201.25) | 0.010 |
| GLU (mmol/L) | 4.61 (4.32, 4.93) | 4.62 (4.34, 4.95) | 4.60 (4.31, 4.92) | 0.454 |
| TG (mmol/L) | 0.92 (0.71, 1.22) | 0.97 (0.75, 1.29) | 0.88 (0.69, 1.19) | <0.001 |
| TC (mmol/L) | 4.05 (3.51, 4.66) | 4.08 (3.61, 4.66) | 4.04 (3.47, 4.65) | 0.131 |
| HDL-C (mmol/L) | 1.26 (1.07, 1.48) | 1.27 (1.08, 1.52) | 1.26 (1.06, 1.47) | 0.008 |
| LDL-C (mmol/L) | 2.38 (2.01, 2.82) | 2.38 (2.01, 2.79) | 2.38 (2.00, 2.83) | 0.748 |
| TG/HDL-C | 0.72 (0.52, 1.02) | 0.74 (0.53, 1.02) | 0.70 (0.51, 1.02) | 0.349 |
| TBA (μmol/L) | 7.80 (4.50, 16.20) | 5.80 (3.70, 9.70) | 9.15 (5.00, 21.92) | <0.001 |
| Lp(a) (mg/L) | 53.75 (26.70, 115.50) | 58.45 (27.50, 129.10) | 51.65 (26.17, 110.93) | 0.079 |
| Hcy (μmol/L) | 11.30 (8.30, 15.80) | 11.10 (8.20, 15.88) | 11.30 (8.30, 15.72) | 0.718 |
| UA (μmol/L) | 222.50 (173.25, 285.75) | 221.50 (174.00, 282.00) | 223.00 (172.75, 286.00) | 0.860 |
| BUN (mmol/) | 4.88 (3.99, 5.90) | 4.93 (4.11, 5.98) | 4.87 (3.95, 5.85) | 0.076 |
| Cr (μmol/L) | 65.20 (54.00, 79.18) | 65.55 (53.12, 82.07) | 64.95 (54.68, 78.23) | 0.541 |
| PⅢNP (ng/mL) | 14.24 (9.92, 23.26) | 12.37 (9.18, 19.11) | 15.21 (10.20, 24.79) | <0.001 |
| LN (ng/mL) | 104.36 (80.90, 140.61) | 98.98 (76.04, 126.21) | 108.03 (82.45, 151.14) | <0.001 |
| HA (ng/mL) | 123.04 (71.61, 226.08) | 102.69 (61.83, 176.16) | 134.66 (77.42, 248.96) | <0.001 |
| CIV (ng/mL) | 66.87 (49.04, 101.60) | 57.42 (40.35, 79.25) | 72.22 (51.76, 109.73) | <0.001 |
| INR | 1.01 (0.95, 1.09) | 1.00 (0.94, 1.07) | 1.02 (0.96, 1.11) | <0.001 |
| FIB (g/L) | 2.08 (1.78, 2.42) | 2.17 (1.86, 2.54) | 2.04 (1.74, 2.38) | <0.001 |
| PT (s) | 31.00 (29.10, 33.10) | 30.70 (28.92, 32.68) | 31.00 (29.10, 33.40) | 0.002 |
| APTT (s) | 11.50 (10.80, 12.40) | 11.30 (10.60, 12.00) | 11.50 (10.90, 12.60) | <0.001 |
| TBIL (μmol/L) | 15.20 (11.30, 20.58) | 13.60 (9.10, 17.98) | 15.91 (12.00, 21.70) | <0.001 |
| DBIL (μmol/L) | 3.20 (2.40, 4.50) | 2.80 (1.90, 3.90) | 3.40 (2.50, 4.73) | <0.001 |
| IBIL (μmol/L) | 11.80 (8.80, 16.00) | 10.30 (7.00, 13.90) | 12.40 (9.47, 17.00) | <0.001 |
| 24-h UCu (μg/24 h) | 748.37 (423.13, 1151.57) | 703.16 (420.82, 1108.14) | 762.30 (427.12, 1162.81) | 0.294 |
Fig.2 Feature selection plot of LASSO regression for screening predictors of fatty liver in WD patients. A: Distribution of LASSO regression coefficients. B: Determination of the regularization parameter (λ) for the LASSO model by 10-fold cross-validation. The left dashed line represents the λmin, and the right dashed line represents λ1se.
Fig.2 Feature selection plot of LASSO regression for screening predictors of fatty liver in WD patients. A: Distribution of LASSO regression coefficients. B: Determination of the regularization parameter (λ) for the LASSO model by 10-fold cross-validation. The left dashed line represents the λmin, and the right dashed line represents λ1se.
Tab.2 Performance comparison of different machine learning models for predicting fatty liver in WD patients
| Model | AUC (95% CI) | Accuracy | Precision | Sensitivity | Specificity | F1 Score | Youden's J | PPV | NPV |
|---|---|---|---|---|---|---|---|---|---|
| Training set | |||||||||
| LR | 0.738 (0.710-0.765) | 0.728 | 0.752 | 0.910 | 0.312 | 0.823 | 0.222 | 0.752 | 0.602 |
| DT | 0.785 (0.759-0.811) | 0.800 | 0.777 | 0.999 | 0.345 | 0.874 | 0.344 | 0.777 | 0.993 |
| RF | 0.840 (0.816-0.863) | 0.802 | 0.780 | 0.994 | 0.360 | 0.875 | 0.355 | 0.780 | 0.966 |
| XGBoost | 0.811 (0.785-0.837) | 0.785 | 0.767 | 0.992 | 0.310 | 0.865 | 0.302 | 0.767 | 0.946 |
| LightGBM | 0.826 (0.801-0.849) | 0.797 | 0.778 | 0.991 | 0.353 | 0.872 | 0.344 | 0.778 | 0.946 |
| SVM | 0.735 (0.706-0.762) | 0.741 | 0.739 | 0.970 | 0.217 | 0.839 | 0.187 | 0.739 | 0.761 |
| ANN | 0.771 (0.744-0.795) | 0.759 | 0.778 | 0.915 | 0.403 | 0.841 | 0.318 | 0.778 | 0.675 |
| Validation set | |||||||||
| LR | 0.729 (0.684-0.772) | 0.713 | 0.751 | 0.879 | 0.331 | 0.810 | 0.210 | 0.751 | 0.544 |
| DT | 0.785 (0.744-0.824) | 0.808 | 0.786 | 0.995 | 0.379 | 0.878 | 0.374 | 0.786 | 0.970 |
| RF | 0.809 (0.770-0.848) | 0.813 | 0.793 | 0.990 | 0.408 | 0.881 | 0.398 | 0.793 | 0.945 |
| XGBoost | 0.804 (0.764-0.843) | 0.799 | 0.780 | 0.990 | 0.361 | 0.873 | 0.351 | 0.780 | 0.938 |
| LightGBM | 0.815 (0.776-0.852) | 0.820 | 0.798 | 0.995 | 0.420 | 0.885 | 0.415 | 0.798 | 0.973 |
| SVM | 0.722 (0.677-0.767) | 0.731 | 0.743 | 0.938 | 0.254 | 0.829 | 0.193 | 0.743 | 0.642 |
| ANN | 0.734 (0.691-0.780) | 0.754 | 0.777 | 0.907 | 0.402 | 0.837 | 0.310 | 0.777 | 0.654 |
Tab.2 Performance comparison of different machine learning models for predicting fatty liver in WD patients
| Model | AUC (95% CI) | Accuracy | Precision | Sensitivity | Specificity | F1 Score | Youden's J | PPV | NPV |
|---|---|---|---|---|---|---|---|---|---|
| Training set | |||||||||
| LR | 0.738 (0.710-0.765) | 0.728 | 0.752 | 0.910 | 0.312 | 0.823 | 0.222 | 0.752 | 0.602 |
| DT | 0.785 (0.759-0.811) | 0.800 | 0.777 | 0.999 | 0.345 | 0.874 | 0.344 | 0.777 | 0.993 |
| RF | 0.840 (0.816-0.863) | 0.802 | 0.780 | 0.994 | 0.360 | 0.875 | 0.355 | 0.780 | 0.966 |
| XGBoost | 0.811 (0.785-0.837) | 0.785 | 0.767 | 0.992 | 0.310 | 0.865 | 0.302 | 0.767 | 0.946 |
| LightGBM | 0.826 (0.801-0.849) | 0.797 | 0.778 | 0.991 | 0.353 | 0.872 | 0.344 | 0.778 | 0.946 |
| SVM | 0.735 (0.706-0.762) | 0.741 | 0.739 | 0.970 | 0.217 | 0.839 | 0.187 | 0.739 | 0.761 |
| ANN | 0.771 (0.744-0.795) | 0.759 | 0.778 | 0.915 | 0.403 | 0.841 | 0.318 | 0.778 | 0.675 |
| Validation set | |||||||||
| LR | 0.729 (0.684-0.772) | 0.713 | 0.751 | 0.879 | 0.331 | 0.810 | 0.210 | 0.751 | 0.544 |
| DT | 0.785 (0.744-0.824) | 0.808 | 0.786 | 0.995 | 0.379 | 0.878 | 0.374 | 0.786 | 0.970 |
| RF | 0.809 (0.770-0.848) | 0.813 | 0.793 | 0.990 | 0.408 | 0.881 | 0.398 | 0.793 | 0.945 |
| XGBoost | 0.804 (0.764-0.843) | 0.799 | 0.780 | 0.990 | 0.361 | 0.873 | 0.351 | 0.780 | 0.938 |
| LightGBM | 0.815 (0.776-0.852) | 0.820 | 0.798 | 0.995 | 0.420 | 0.885 | 0.415 | 0.798 | 0.973 |
| SVM | 0.722 (0.677-0.767) | 0.731 | 0.743 | 0.938 | 0.254 | 0.829 | 0.193 | 0.743 | 0.642 |
| ANN | 0.734 (0.691-0.780) | 0.754 | 0.777 | 0.907 | 0.402 | 0.837 | 0.310 | 0.777 | 0.654 |
Fig.3 AUC, calibration curves and decision curves of fatty liver prediction models for WD patients in the training and validation sets. A: Training-set AUC. B: Validation-set AUC. C: Training-set calibration curves. D: Validation-set calibration curves. E: Training-set decision curves. F: Validation-set decision curves.
Fig.3 AUC, calibration curves and decision curves of fatty liver prediction models for WD patients in the training and validation sets. A: Training-set AUC. B: Validation-set AUC. C: Training-set calibration curves. D: Validation-set calibration curves. E: Training-set decision curves. F: Validation-set decision curves.
Fig.4 PR curve of the prediction model for fatty liver in WD patients in the validation set. X-axis: Recall; Y-axis: Precision. Colored curves: machine learning models (annotated with AP values, higher = better performance). Red dashed line: sample prevalence (69.7%, random guessing baseline).
Fig.4 PR curve of the prediction model for fatty liver in WD patients in the validation set. X-axis: Recall; Y-axis: Precision. Colored curves: machine learning models (annotated with AP values, higher = better performance). Red dashed line: sample prevalence (69.7%, random guessing baseline).
Fig.5 SHAP interpretability analysis of the LightGBM model for predicting fatty liver in WD patients. A: Beeswarm plot of SHAP values for each feature. Each point represents a sample. Greater dispersion of points indicates a more significant impact on the model output. Colors represent feature values (red=high, blue=low). B: Bar plot of mean absolute SHAP values. Each row represents a feature, with higher values indicating greater overall contribution to the model prediction. C: Waterfall plot of SHAP values for a single sample. Baseline E[f(X)] denotes the expected model output, and final f(x)denotes the sample-specific prediction. Red bars represent positive SHAP contributions (pushing the prediction upward), while blue bars represent negative contributions (pushing the prediction downward). Labels show each feature and its corresponding SHAP value.
Fig.5 SHAP interpretability analysis of the LightGBM model for predicting fatty liver in WD patients. A: Beeswarm plot of SHAP values for each feature. Each point represents a sample. Greater dispersion of points indicates a more significant impact on the model output. Colors represent feature values (red=high, blue=low). B: Bar plot of mean absolute SHAP values. Each row represents a feature, with higher values indicating greater overall contribution to the model prediction. C: Waterfall plot of SHAP values for a single sample. Baseline E[f(X)] denotes the expected model output, and final f(x)denotes the sample-specific prediction. Red bars represent positive SHAP contributions (pushing the prediction upward), while blue bars represent negative contributions (pushing the prediction downward). Labels show each feature and its corresponding SHAP value.
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