Title: Machine learning for predicting all-cause mortality of metabolic dysfunction-associated
fatty liver disease: a longitudinal study based on NHANES
Version: 1 Date: 25 Mar 2025
Reviewer's report:
1. A dedicated literature review or related works section should be added to review similar
studies relevant to this manuscript.
2. The dataset's degree of skewness and kurtosis should be reported.
3. Methods for balancing the dataset should be considered to equalize the number of samples
across different classes.
4. Given the lower mortality rate in the dataset, was any technique other than 10-fold cross-
validation used to mitigate potential bias and error in the machine learning models? Please
clarify.
5. A detailed description of the machine learning models used in this study should be provided.
6. Additional evaluation metrics, such as F1-score, NPV, PPV, and accuracy, should be
incorporated to provide a more comprehensive assessment of model performance.
7. The discussion section requires further elaboration on the application and effectiveness
of machine learning models in this field. The findings should be analyzed in greater
depth, incorporating perspectives from physicians, and the results should be compared more
comprehensively with similar studies.
8. The advantages and disadvantages of the proposed method should be explicitly stated in the
conclusion section.
9. Since this manuscript primarily employs common machine learning algorithms for predicting
all-cause mortality in metabolic dysfunction-associated fatty liver disease, its technological
innovation is limited. Additional machine learning and deep learning models, such as support
vector machines, k-nearest neighbors, multilayer perception, etc., are recommended to enhance
novelty. Moreover, metaheuristic algorithms like the bat and genetic algorithms could be applied
for feature selection, with the results compared to the current findings.
Title: Machine learning for predicting all-cause mortality of metabolic dysfunction-associated
fatty liver disease: a longitudinal study based on NHANES
Version: 1 Date: 25 Mar 2025
Reviewer's report:
1. A dedicated literature review or related works section should be added to review similar
studies relevant to this manuscript.
2. The dataset's degree of skewness and kurtosis should be reported.
3. Methods for balancing the dataset should be considered to equalize the number of samples
across different classes.
4. Given the lower mortality rate in the dataset, was any technique other than 10-fold cross-
validation used to mitigate potential bias and error in the machine learning models? Please
clarify.
5. A detailed description of the machine learning models used in this study should be provided.
6. Additional evaluation metrics, such as F1-score, NPV, PPV, and accuracy, should be
incorporated to provide a more comprehensive assessment of model performance.
7. The discussion section requires further elaboration on the application and effectiveness
of machine learning models in this field. The findings should be analyzed in greater
depth, incorporating perspectives from physicians, and the results should be compared more
comprehensively with similar studies.
8. The advantages and disadvantages of the proposed method should be explicitly stated in the
conclusion section.
9. Since this manuscript primarily employs common machine learning algorithms for predicting
all-cause mortality in metabolic dysfunction-associated fatty liver disease, its technological
innovation is limited. Additional machine learning and deep learning models, such as support
vector machines, k-nearest neighbors, multilayer perception, etc., are recommended to enhance
novelty. Moreover, metaheuristic algorithms like the bat and genetic algorithms could be applied
for feature selection, with the results compared to the current findings.
Please review this paper.
user
Title: Machine learning for predicting all-cause mortality of metabolic dysfunction-associated
fatty liver disease: a longitudinal study based on NHANES
Version: 1 Date: 25 Mar 2025
Reviewer's report:
1. A dedicated literature review or related works section should be added to review similar
studies relevant to this manuscript.
2. The dataset's degree of skewness and kurtosis should be reported.
3. Methods for balancing the dataset should be considered to equalize the number of samples
across different classes.
4. Given the lower mortality rate in the dataset, was any technique other than 10-fold cross-
validation used to mitigate potential bias and error in the machine learning models? Please
clarify.
5. A detailed description of the machine learning models used in this study should be provided.
6. Additional evaluation metrics, such as F1-score, NPV, PPV, and accuracy, should be
incorporated to provide a more comprehensive assessment of model performance.
7. The discussion section requires further elaboration on the application and effectiveness
of machine learning models in this field. The findings should be analyzed in greater
depth, incorporating perspectives from physicians, and the results should be compared more
comprehensively with similar studies.
8. The advantages and disadvantages of the proposed method should be explicitly stated in the
conclusion section.
9. Since this manuscript primarily employs common machine learning algorithms for predicting
all-cause mortality in metabolic dysfunction-associated fatty liver disease, its technological
innovation is limited. Additional machine learning and deep learning models, such as support
vector machines, k-nearest neighbors, multilayer perception, etc., are recommended to enhance
novelty. Moreover, metaheuristic algorithms like the bat and genetic algorithms could be applied
for feature selection, with the results compared to the current findings.
assistant