Title: Machine Learning-Based Disease Risk Stratification and Prediction of Metabolic
Dysfunction-Associated Fatty Liver Disease Using Vibration-Controlled Transient
Elastography: Result from NHANES 2021–2023
Version: 1 Date: 17 Mar 2025
Reviewer:  Marie Raymond
Reviewer's report:
The authors present a machine learning model for stratifying MAFLD risk (low, moderate, high)
using NHANES 2021–2023 data. Key features identified include demographic, clinical, and
socioeconomic factors. The random forest model achieved a micro-average AUC of 0.80, with
strong performance for low- (AUC 0.83) and high-risk (AUC 0.79) groups but lower accuracy
for moderate-risk (AUC 0.66). The study highlights the potential of integrating VCTE with
accessible clinical variables for population-level screening.  The study have  several strengths:
1. Utilizes NHANES, a large, nationally representative dataset.
2. Rigorous exclusion criteria and appropriate handling of missing data.
3.Combines LASSO for feature selection and random forest for classification, aligning with best
practices.
4.  Identifies key predictors (e.g., BMI, waist circumference, socioeconomic factors) consistent
with existing literature.
5.Emphasizes practical application for non-invasive screening in resource-limited settings.
6. Detailed reporting of participant characteristics and model performance metrics (AUC,
confusion matrix).
7. Acknowledges limitations, including cross-sectional design and unmeasured confounders.
Comments for improvement:
1.The model’s lower AUC (0.66) for moderate-risk groups suggests challenges in distinguishing
overlapping criteria (e.g., CAP/LSM combinations). This warrants deeper analysis, such as
feature importance for this subgroup.
2. Table 1: The hemoglobin value under "High risk" is reported as **49.08 ± 12.89 g/
dL**, which is physiologically implausible. This likely reflects a typographical error (possibly
swapped with HDL-C or another variable).
3. The manuscript describes 241 correct classifications for non-fibrotic MAFLD, but the
provided matrix (236, 92, 11, etc.) lacks clear labeling, making validation difficult.
4. Limited comparison to established risk scores (e.g., FIB-4, NAFLD Fibrosis Score) weakens
the discussion of novelty.
5. No mention of how the model’s performance translates to clinical decision-making (e.g., cost-
effectiveness, screening thresholds).
6. Residual analysis mentions a "minor downward bias" in high-value regions but does not
explore its implications for model generalizability.
7. The rationale for selecting random forest over other algorithms (e.g., gradient boosting) is
not discussed.
8. Label rows/columns in the confusion matrix (provided in TIFF) to clarify true vs. predicted
classes.
9. Discuss strategies to enhance moderate-risk classification (e.g., stratified sampling, ensemble
methods).
10. Compare model performance to existing risk scores and justify its clinical utility.
11.  Elaborate on the clinical implications of socioeconomic factors (e.g., how income/education
disparities impact MAFLD risk).
12.  Address the limitations of VCTE in obese populations and how the model mitigates this.
13. Additional Notes:
- Ensure consistency in formatting (e.g., decimal places, units).
- Proofread for minor grammatical errors (e.g., "Participants was enrolled" # "were enrolled").
- Consider adding a supplemental figure showing the distribution of excluded participants.
In summary, This manuscript has the potential to contribute meaningfully to hepatology and
preventive medicine but requires revisions to meet publication standards.
Title: Machine Learning-Based Disease Risk Stratification and Prediction of Metabolic
Dysfunction-Associated Fatty Liver Disease Using Vibration-Controlled Transient
Elastography: Result from NHANES 2021–2023
Version: 1 Date: 17 Mar 2025
Reviewer:  Marie Raymond
Reviewer's report:
The authors present a machine learning model for stratifying MAFLD risk (low, moderate, high)
using NHANES 2021–2023 data. Key features identified include demographic, clinical, and
socioeconomic factors. The random forest model achieved a micro-average AUC of 0.80, with
strong performance for low- (AUC 0.83) and high-risk (AUC 0.79) groups but lower accuracy
for moderate-risk (AUC 0.66). The study highlights the potential of integrating VCTE with
accessible clinical variables for population-level screening.  The study have  several strengths:
1. Utilizes NHANES, a large, nationally representative dataset.
2. Rigorous exclusion criteria and appropriate handling of missing data.
3.Combines LASSO for feature selection and random forest for classification, aligning with best
practices.
4.  Identifies key predictors (e.g., BMI, waist circumference, socioeconomic factors) consistent
with existing literature.
5.Emphasizes practical application for non-invasive screening in resource-limited settings.
6. Detailed reporting of participant characteristics and model performance metrics (AUC,
confusion matrix).
7. Acknowledges limitations, including cross-sectional design and unmeasured confounders.
Comments for improvement:
1.The model’s lower AUC (0.66) for moderate-risk groups suggests challenges in distinguishing
overlapping criteria (e.g., CAP/LSM combinations). This warrants deeper analysis, such as
feature importance for this subgroup.
2. Table 1: The hemoglobin value under "High risk" is reported as **49.08 ± 12.89 g/
dL**, which is physiologically implausible. This likely reflects a typographical error (possibly
swapped with HDL-C or another variable).
3. The manuscript describes 241 correct classifications for non-fibrotic MAFLD, but the
provided matrix (236, 92, 11, etc.) lacks clear labeling, making validation difficult.
4. Limited comparison to established risk scores (e.g., FIB-4, NAFLD Fibrosis Score) weakens
the discussion of novelty.
5. No mention of how the model’s performance translates to clinical decision-making (e.g., cost-
effectiveness, screening thresholds).
6. Residual analysis mentions a "minor downward bias" in high-value regions but does not
explore its implications for model generalizability.
7. The rationale for selecting random forest over other algorithms (e.g., gradient boosting) is
not discussed.
8. Label rows/columns in the confusion matrix (provided in TIFF) to clarify true vs. predicted
classes.
9. Discuss strategies to enhance moderate-risk classification (e.g., stratified sampling, ensemble
methods).
10. Compare model performance to existing risk scores and justify its clinical utility.
11.  Elaborate on the clinical implications of socioeconomic factors (e.g., how income/education
disparities impact MAFLD risk).
12.  Address the limitations of VCTE in obese populations and how the model mitigates this.
13. Additional Notes:
- Ensure consistency in formatting (e.g., decimal places, units).
- Proofread for minor grammatical errors (e.g., "Participants was enrolled" # "were enrolled").
- Consider adding a supplemental figure showing the distribution of excluded participants.
In summary, This manuscript has the potential to contribute meaningfully to hepatology and
preventive medicine but requires revisions to meet publication standards.
Please review this paper.
user
Title: Machine Learning-Based Disease Risk Stratification and Prediction of Metabolic
Dysfunction-Associated Fatty Liver Disease Using Vibration-Controlled Transient
Elastography: Result from NHANES 2021–2023
Version: 1 Date: 17 Mar 2025
Reviewer:  Marie Raymond
Reviewer's report:
The authors present a machine learning model for stratifying MAFLD risk (low, moderate, high)
using NHANES 2021–2023 data. Key features identified include demographic, clinical, and
socioeconomic factors. The random forest model achieved a micro-average AUC of 0.80, with
strong performance for low- (AUC 0.83) and high-risk (AUC 0.79) groups but lower accuracy
for moderate-risk (AUC 0.66). The study highlights the potential of integrating VCTE with
accessible clinical variables for population-level screening.  The study have  several strengths:
1. Utilizes NHANES, a large, nationally representative dataset.
2. Rigorous exclusion criteria and appropriate handling of missing data.
3.Combines LASSO for feature selection and random forest for classification, aligning with best
practices.
4.  Identifies key predictors (e.g., BMI, waist circumference, socioeconomic factors) consistent
with existing literature.
5.Emphasizes practical application for non-invasive screening in resource-limited settings.
6. Detailed reporting of participant characteristics and model performance metrics (AUC,
confusion matrix).
7. Acknowledges limitations, including cross-sectional design and unmeasured confounders.
Comments for improvement:
1.The model’s lower AUC (0.66) for moderate-risk groups suggests challenges in distinguishing
overlapping criteria (e.g., CAP/LSM combinations). This warrants deeper analysis, such as
feature importance for this subgroup.
2. Table 1: The hemoglobin value under "High risk" is reported as **49.08 ± 12.89 g/
dL**, which is physiologically implausible. This likely reflects a typographical error (possibly
swapped with HDL-C or another variable).
3. The manuscript describes 241 correct classifications for non-fibrotic MAFLD, but the
provided matrix (236, 92, 11, etc.) lacks clear labeling, making validation difficult.
4. Limited comparison to established risk scores (e.g., FIB-4, NAFLD Fibrosis Score) weakens
the discussion of novelty.
5. No mention of how the model’s performance translates to clinical decision-making (e.g., cost-
effectiveness, screening thresholds).
6. Residual analysis mentions a "minor downward bias" in high-value regions but does not
explore its implications for model generalizability.
7. The rationale for selecting random forest over other algorithms (e.g., gradient boosting) is
not discussed.
8. Label rows/columns in the confusion matrix (provided in TIFF) to clarify true vs. predicted
classes.
9. Discuss strategies to enhance moderate-risk classification (e.g., stratified sampling, ensemble
methods).
10. Compare model performance to existing risk scores and justify its clinical utility.
11.  Elaborate on the clinical implications of socioeconomic factors (e.g., how income/education
disparities impact MAFLD risk).
12.  Address the limitations of VCTE in obese populations and how the model mitigates this.
13. Additional Notes:
- Ensure consistency in formatting (e.g., decimal places, units).
- Proofread for minor grammatical errors (e.g., "Participants was enrolled" # "were enrolled").
- Consider adding a supplemental figure showing the distribution of excluded participants.
In summary, This manuscript has the potential to contribute meaningfully to hepatology and
preventive medicine but requires revisions to meet publication standards.
assistant