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: 16 Mar 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-Although NHANES is representative of the U.S. population, the study's reliance on data from a
single cohort may introduce biases related to socioeconomic, ethnic, and geographic factors that
affect disease progression. The generalizability of the findings to other populations, especially
non-U.S. cohorts, remains uncertain.
-While the use of random forest algorithms and LASSO regression is appropriate, these models,
while effective, may become difficult to interpret. For clinicians, complex models can sometimes
act as a "black box," making it harder to understand the rationale behind predictions, which may
reduce their adoption in clinical practice.
-There is no mention of model interpretability tools or efforts to provide more explainable
outputs, which could make the model more accessible to healthcare providers.
-Although the study acknowledges VCTE’s limitations, especially in obese patients, it could
expand more on potential confounding factors such as inflammation, comorbidities, or other
factors that affect liver stiffness readings. Greater attention to these limitations could help clarify
the model’s performance across different patient subgroups.
-The study does not provide much detail on steps taken to prevent overfitting, aside from
feature selection via LASSO. Although this is a useful method, further validation using cross-
validation or an independent validation dataset could provide more confidence in the model's
generalizability.
-The model’s performance is evaluated using NHANES data, but external validation is not
discussed. Validation of the model in independent cohorts or external datasets would be essential
to establish its robustness and reproducibility. Without this, the model's real-world applicability
remains uncertain.
-The study covers a broad range of clinical, demographic, and laboratory data, but there
could be other variables, such as genetic markers or more detailed lifestyle factors (e.g., diet,
physical activity), that might improve predictive performance. Including these variables in future
iterations of the study could enhance model accuracy.
-While the discussion mentions significant associations with features like BMI and waist
circumference, more detailed information on the statistical tests and results would enhance the
discussion. For instance, providing the actual regression coefficients or p-values would give
readers more confidence in the strength of these associations.
-While it's understandable that the cross-sectional nature of NHANES limits causal inferences,
the study could mention potential avenues for future research involving longitudinal datasets.
Including suggestions on how future studies could track participants over time would help set
the stage for more comprehensive analyses.
-The study acknowledges the need for external validation but doesn't propose how to carry out
this validation. Future directions could include testing the model on other cohorts or through a
multi-center approach, which could help confirm the model’s robustness and scalability.
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: 16 Mar 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-Although NHANES is representative of the U.S. population, the study's reliance on data from a
single cohort may introduce biases related to socioeconomic, ethnic, and geographic factors that
affect disease progression. The generalizability of the findings to other populations, especially
non-U.S. cohorts, remains uncertain.
-While the use of random forest algorithms and LASSO regression is appropriate, these models,
while effective, may become difficult to interpret. For clinicians, complex models can sometimes
act as a "black box," making it harder to understand the rationale behind predictions, which may
reduce their adoption in clinical practice.
-There is no mention of model interpretability tools or efforts to provide more explainable
outputs, which could make the model more accessible to healthcare providers.
-Although the study acknowledges VCTE’s limitations, especially in obese patients, it could
expand more on potential confounding factors such as inflammation, comorbidities, or other
factors that affect liver stiffness readings. Greater attention to these limitations could help clarify
the model’s performance across different patient subgroups.
-The study does not provide much detail on steps taken to prevent overfitting, aside from
feature selection via LASSO. Although this is a useful method, further validation using cross-
validation or an independent validation dataset could provide more confidence in the model's
generalizability.
-The model’s performance is evaluated using NHANES data, but external validation is not
discussed. Validation of the model in independent cohorts or external datasets would be essential
to establish its robustness and reproducibility. Without this, the model's real-world applicability
remains uncertain.
-The study covers a broad range of clinical, demographic, and laboratory data, but there
could be other variables, such as genetic markers or more detailed lifestyle factors (e.g., diet,
physical activity), that might improve predictive performance. Including these variables in future
iterations of the study could enhance model accuracy.
-While the discussion mentions significant associations with features like BMI and waist
circumference, more detailed information on the statistical tests and results would enhance the
discussion. For instance, providing the actual regression coefficients or p-values would give
readers more confidence in the strength of these associations.
-While it's understandable that the cross-sectional nature of NHANES limits causal inferences,
the study could mention potential avenues for future research involving longitudinal datasets.
Including suggestions on how future studies could track participants over time would help set
the stage for more comprehensive analyses.
-The study acknowledges the need for external validation but doesn't propose how to carry out
this validation. Future directions could include testing the model on other cohorts or through a
multi-center approach, which could help confirm the model’s robustness and scalability.
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: 16 Mar 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-Although NHANES is representative of the U.S. population, the study's reliance on data from a
single cohort may introduce biases related to socioeconomic, ethnic, and geographic factors that
affect disease progression. The generalizability of the findings to other populations, especially
non-U.S. cohorts, remains uncertain.
-While the use of random forest algorithms and LASSO regression is appropriate, these models,
while effective, may become difficult to interpret. For clinicians, complex models can sometimes
act as a "black box," making it harder to understand the rationale behind predictions, which may
reduce their adoption in clinical practice.
-There is no mention of model interpretability tools or efforts to provide more explainable
outputs, which could make the model more accessible to healthcare providers.
-Although the study acknowledges VCTE’s limitations, especially in obese patients, it could
expand more on potential confounding factors such as inflammation, comorbidities, or other
factors that affect liver stiffness readings. Greater attention to these limitations could help clarify
the model’s performance across different patient subgroups.
-The study does not provide much detail on steps taken to prevent overfitting, aside from
feature selection via LASSO. Although this is a useful method, further validation using cross-
validation or an independent validation dataset could provide more confidence in the model's
generalizability.
-The model’s performance is evaluated using NHANES data, but external validation is not
discussed. Validation of the model in independent cohorts or external datasets would be essential
to establish its robustness and reproducibility. Without this, the model's real-world applicability
remains uncertain.
-The study covers a broad range of clinical, demographic, and laboratory data, but there
could be other variables, such as genetic markers or more detailed lifestyle factors (e.g., diet,
physical activity), that might improve predictive performance. Including these variables in future
iterations of the study could enhance model accuracy.
-While the discussion mentions significant associations with features like BMI and waist
circumference, more detailed information on the statistical tests and results would enhance the
discussion. For instance, providing the actual regression coefficients or p-values would give
readers more confidence in the strength of these associations.
-While it's understandable that the cross-sectional nature of NHANES limits causal inferences,
the study could mention potential avenues for future research involving longitudinal datasets.
Including suggestions on how future studies could track participants over time would help set
the stage for more comprehensive analyses.
-The study acknowledges the need for external validation but doesn't propose how to carry out
this validation. Future directions could include testing the model on other cohorts or through a
multi-center approach, which could help confirm the model’s robustness and scalability.
assistant