Title: Machine Learning Survival Models for Non-alcoholic Fatty Liver Disease Based on a
Health Checkup Cohort
Version: 0 Date: 31 May 2025
Reviewer's report:
This manuscript explores a highly relevant public health issue—predicting the risk of NAFLD
—using a survival analysis approach with machine learning (Random Survival Forest, RSF)
compared to the traditional Cox regression. The study benefits from a large sample size and
a well-structured methodology. However, there are several areas where the manuscript can be
strengthened to improve clarity, rigor, and completeness.
Abstract
• Comment: The abstract is comprehensive but overly dense with numerical results. Consider
simplifying the language and reducing the number of specific metrics to highlight the main
findings.
• Suggestion: Emphasize the novelty of using survival models (especially RSF) for NAFLD risk
prediction early in the abstract to distinguish it from previous studies.
Introduction
• Comment: The rationale for using time-to-event models (survival analysis) is well introduced.
However, the existing literature gap could be clarified more directly.
• Suggestion: Consider rewording the last paragraph of the Introduction to better articulate the
novelty: e.g., “Unlike previous diagnostic or cross-sectional prediction studies, this study is the
first to utilize time-to-event machine learning approaches...”
Methods
Section 2.1 – Study Population
• Comment: The inclusion and exclusion criteria are well-described. However, the justification
for excluding people with diabetes should be clarified, given their high risk of NAFLD.
• Suggestion: Add a brief rationale for excluding people with diabetes—was it to focus on an
ostensibly healthy population?
Section 2.3 – Statistical Analysis
• Comment: The feature selection and modeling procedures are robust and thoroughly described.
• Suggestion: Clarify whether any imputation was performed for missing data prior to LASSO
(even though exclusion is mentioned).
Results
Section 3.1 – Baseline Characteristics
• Comment: Table 1 presents an informative breakdown by outcome. However, the structure
could be improved for readability (e.g., align headers clearly with groups).
• Suggestion: Consider renaming “Censored” to “Non-NAFLD” to improve accessibility for
non-statistical readers.
Section 3.2.1 – Feature Selection
• Comment: Figure 2 is informative, but LASSO selection should be discussed in terms of
biological plausibility as well.
• Suggestion: Consider including a brief discussion on how the selected variables align with
established NAFLD risk factors.
Section 3.2.2 – Model Development
• Comment: Table 2 effectively shows variable importance. However, the labeling “RSF model”
contains a typo.
• Suggestion: Correct the label to “RSF model”.
Section 3.2.3 – Model Evaluation
• Comment: Figure 3 and Table 3 demonstrate the superior performance of RSF. Still, confidence
intervals for calibration metrics are wide.
• Suggestion: Discuss possible reasons for the relatively high iBS for both models and how it
impacts practical utility.
Section 3.2.4 – Survival Curves Analysis
• Comment: Figure 4 displays strong stratification; however, it is unclear how the cut-off point
was determined.
• Suggestion: Expand the explanation of the “survminer” package and thresholding method used.
Section 3.2.5 – Model Interpretation
• Comment: Figure 5, showing partial dependence plots, is helpful. However, interpretation
could be made more accessible to a general audience.
• Suggestion: Simplify the explanation—e.g., “BMI and TG had the strongest impact on survival
predictions, with wide variation in risk based on small changes in values.”
Discussion
• Comment: The Discussion effectively contrasts RSF and Cox models, citing supporting
literature. However, the study population size is inconsistently reported (e.g., 12,580 vs. 18,250).
• Suggestion: Ensure consistency in the description of the population throughout.
• Comment: The limitations are superficial.
• Suggestion: Explicitly state the limitation of potential residual confounding and lack of external
validation.
Figure 6 – Prediction Distribution
• Comment: The figure is visually compelling, but the interpretation is vague.
• Suggestion: Elaborate on the clinical or methodological significance of misclassification after
the 30-month point.
Conclusion
• Comment: The conclusion is appropriate, though it essentially repeats prior content.
• Suggestion: Consider summarizing key implications in one impactful sentence—e.g.,
“This RSF model enables early identification and proactive health management of high-risk
individuals in general populations.”
Tables and Figures
• Table 1: Consider reordering columns to “NAFLD / Non-NAFLD / Overall” for better narrative
alignment.
• Table 2: Adjust table formatting to eliminate layout inconsistency between models.
• Figure 3: Include sub-figure labels (3A, 3B, etc.) in the caption for clarity.
• Figure 5: Add axis labels and variable names where relevant for more straightforward
interpretation.
Minor Technical Issues
• Typo: “RSF mdoel” # “RSF model” (Table 2)
• Consistency: 18,250 vs. 12,580 subjects in Discussion.
• Language: Some minor grammatical inconsistencies (e.g., “trying to see clear the
relationships…” # “to examine the relationships…”).
Title: Machine Learning Survival Models for Non-alcoholic Fatty Liver Disease Based on a
Health Checkup Cohort
Version: 0 Date: 31 May 2025
Reviewer's report:
This manuscript explores a highly relevant public health issue—predicting the risk of NAFLD
—using a survival analysis approach with machine learning (Random Survival Forest, RSF)
compared to the traditional Cox regression. The study benefits from a large sample size and
a well-structured methodology. However, there are several areas where the manuscript can be
strengthened to improve clarity, rigor, and completeness.
Abstract
• Comment: The abstract is comprehensive but overly dense with numerical results. Consider
simplifying the language and reducing the number of specific metrics to highlight the main
findings.
• Suggestion: Emphasize the novelty of using survival models (especially RSF) for NAFLD risk
prediction early in the abstract to distinguish it from previous studies.
Introduction
• Comment: The rationale for using time-to-event models (survival analysis) is well introduced.
However, the existing literature gap could be clarified more directly.
• Suggestion: Consider rewording the last paragraph of the Introduction to better articulate the
novelty: e.g., “Unlike previous diagnostic or cross-sectional prediction studies, this study is the
first to utilize time-to-event machine learning approaches...”
Methods
Section 2.1 – Study Population
• Comment: The inclusion and exclusion criteria are well-described. However, the justification
for excluding people with diabetes should be clarified, given their high risk of NAFLD.
• Suggestion: Add a brief rationale for excluding people with diabetes—was it to focus on an
ostensibly healthy population?
Section 2.3 – Statistical Analysis
• Comment: The feature selection and modeling procedures are robust and thoroughly described.
• Suggestion: Clarify whether any imputation was performed for missing data prior to LASSO
(even though exclusion is mentioned).
Results
Section 3.1 – Baseline Characteristics
• Comment: Table 1 presents an informative breakdown by outcome. However, the structure
could be improved for readability (e.g., align headers clearly with groups).
• Suggestion: Consider renaming “Censored” to “Non-NAFLD” to improve accessibility for
non-statistical readers.
Section 3.2.1 – Feature Selection
• Comment: Figure 2 is informative, but LASSO selection should be discussed in terms of
biological plausibility as well.
• Suggestion: Consider including a brief discussion on how the selected variables align with
established NAFLD risk factors.
Section 3.2.2 – Model Development
• Comment: Table 2 effectively shows variable importance. However, the labeling “RSF model”
contains a typo.
• Suggestion: Correct the label to “RSF model”.
Section 3.2.3 – Model Evaluation
• Comment: Figure 3 and Table 3 demonstrate the superior performance of RSF. Still, confidence
intervals for calibration metrics are wide.
• Suggestion: Discuss possible reasons for the relatively high iBS for both models and how it
impacts practical utility.
Section 3.2.4 – Survival Curves Analysis
• Comment: Figure 4 displays strong stratification; however, it is unclear how the cut-off point
was determined.
• Suggestion: Expand the explanation of the “survminer” package and thresholding method used.
Section 3.2.5 – Model Interpretation
• Comment: Figure 5, showing partial dependence plots, is helpful. However, interpretation
could be made more accessible to a general audience.
• Suggestion: Simplify the explanation—e.g., “BMI and TG had the strongest impact on survival
predictions, with wide variation in risk based on small changes in values.”
Discussion
• Comment: The Discussion effectively contrasts RSF and Cox models, citing supporting
literature. However, the study population size is inconsistently reported (e.g., 12,580 vs. 18,250).
• Suggestion: Ensure consistency in the description of the population throughout.
• Comment: The limitations are superficial.
• Suggestion: Explicitly state the limitation of potential residual confounding and lack of external
validation.
Figure 6 – Prediction Distribution
• Comment: The figure is visually compelling, but the interpretation is vague.
• Suggestion: Elaborate on the clinical or methodological significance of misclassification after
the 30-month point.
Conclusion
• Comment: The conclusion is appropriate, though it essentially repeats prior content.
• Suggestion: Consider summarizing key implications in one impactful sentence—e.g.,
“This RSF model enables early identification and proactive health management of high-risk
individuals in general populations.”
Tables and Figures
• Table 1: Consider reordering columns to “NAFLD / Non-NAFLD / Overall” for better narrative
alignment.
• Table 2: Adjust table formatting to eliminate layout inconsistency between models.
• Figure 3: Include sub-figure labels (3A, 3B, etc.) in the caption for clarity.
• Figure 5: Add axis labels and variable names where relevant for more straightforward
interpretation.
Minor Technical Issues
• Typo: “RSF mdoel” # “RSF model” (Table 2)
• Consistency: 18,250 vs. 12,580 subjects in Discussion.
• Language: Some minor grammatical inconsistencies (e.g., “trying to see clear the
relationships…” # “to examine the relationships…”).
Please review this paper.
user
Title: Machine Learning Survival Models for Non-alcoholic Fatty Liver Disease Based on a
Health Checkup Cohort
Version: 0 Date: 31 May 2025
Reviewer's report:
This manuscript explores a highly relevant public health issue—predicting the risk of NAFLD
—using a survival analysis approach with machine learning (Random Survival Forest, RSF)
compared to the traditional Cox regression. The study benefits from a large sample size and
a well-structured methodology. However, there are several areas where the manuscript can be
strengthened to improve clarity, rigor, and completeness.
Abstract
• Comment: The abstract is comprehensive but overly dense with numerical results. Consider
simplifying the language and reducing the number of specific metrics to highlight the main
findings.
• Suggestion: Emphasize the novelty of using survival models (especially RSF) for NAFLD risk
prediction early in the abstract to distinguish it from previous studies.
Introduction
• Comment: The rationale for using time-to-event models (survival analysis) is well introduced.
However, the existing literature gap could be clarified more directly.
• Suggestion: Consider rewording the last paragraph of the Introduction to better articulate the
novelty: e.g., “Unlike previous diagnostic or cross-sectional prediction studies, this study is the
first to utilize time-to-event machine learning approaches...”
Methods
Section 2.1 – Study Population
• Comment: The inclusion and exclusion criteria are well-described. However, the justification
for excluding people with diabetes should be clarified, given their high risk of NAFLD.
• Suggestion: Add a brief rationale for excluding people with diabetes—was it to focus on an
ostensibly healthy population?
Section 2.3 – Statistical Analysis
• Comment: The feature selection and modeling procedures are robust and thoroughly described.
• Suggestion: Clarify whether any imputation was performed for missing data prior to LASSO
(even though exclusion is mentioned).
Results
Section 3.1 – Baseline Characteristics
• Comment: Table 1 presents an informative breakdown by outcome. However, the structure
could be improved for readability (e.g., align headers clearly with groups).
• Suggestion: Consider renaming “Censored” to “Non-NAFLD” to improve accessibility for
non-statistical readers.
Section 3.2.1 – Feature Selection
• Comment: Figure 2 is informative, but LASSO selection should be discussed in terms of
biological plausibility as well.
• Suggestion: Consider including a brief discussion on how the selected variables align with
established NAFLD risk factors.
Section 3.2.2 – Model Development
• Comment: Table 2 effectively shows variable importance. However, the labeling “RSF model”
contains a typo.
• Suggestion: Correct the label to “RSF model”.
Section 3.2.3 – Model Evaluation
• Comment: Figure 3 and Table 3 demonstrate the superior performance of RSF. Still, confidence
intervals for calibration metrics are wide.
• Suggestion: Discuss possible reasons for the relatively high iBS for both models and how it
impacts practical utility.
Section 3.2.4 – Survival Curves Analysis
• Comment: Figure 4 displays strong stratification; however, it is unclear how the cut-off point
was determined.
• Suggestion: Expand the explanation of the “survminer” package and thresholding method used.
Section 3.2.5 – Model Interpretation
• Comment: Figure 5, showing partial dependence plots, is helpful. However, interpretation
could be made more accessible to a general audience.
• Suggestion: Simplify the explanation—e.g., “BMI and TG had the strongest impact on survival
predictions, with wide variation in risk based on small changes in values.”
Discussion
• Comment: The Discussion effectively contrasts RSF and Cox models, citing supporting
literature. However, the study population size is inconsistently reported (e.g., 12,580 vs. 18,250).
• Suggestion: Ensure consistency in the description of the population throughout.
• Comment: The limitations are superficial.
• Suggestion: Explicitly state the limitation of potential residual confounding and lack of external
validation.
Figure 6 – Prediction Distribution
• Comment: The figure is visually compelling, but the interpretation is vague.
• Suggestion: Elaborate on the clinical or methodological significance of misclassification after
the 30-month point.
Conclusion
• Comment: The conclusion is appropriate, though it essentially repeats prior content.
• Suggestion: Consider summarizing key implications in one impactful sentence—e.g.,
“This RSF model enables early identification and proactive health management of high-risk
individuals in general populations.”
Tables and Figures
• Table 1: Consider reordering columns to “NAFLD / Non-NAFLD / Overall” for better narrative
alignment.
• Table 2: Adjust table formatting to eliminate layout inconsistency between models.
• Figure 3: Include sub-figure labels (3A, 3B, etc.) in the caption for clarity.
• Figure 5: Add axis labels and variable names where relevant for more straightforward
interpretation.
Minor Technical Issues
• Typo: “RSF mdoel” # “RSF model” (Table 2)
• Consistency: 18,250 vs. 12,580 subjects in Discussion.
• Language: Some minor grammatical inconsistencies (e.g., “trying to see clear the
relationships…” # “to examine the relationships…”).
assistant