Title: Efficient diagnosis for endoscopic remission in Crohn’s diseases by the combination of
three non-invasive markers
Version: 3 Date: 31 Jan 2025
Reviewer's report:
I thank the reviewers for addressing the previous comments by amending their manuscript. The
methods and results sections are now clearer, which will enhance readers' acceptance of the
article.
However, I still find the following text problematic:
“Two different classifiers were adopted: k-fold cross-validation as a resampling method and a
support vector machine (SVM) to validate the diagnostic ability of multiple markers. Regarding
k-fold cross-validation, we performed cross-validation from 2-fold to 10-fold on each subject's
data and adopted the k value with the highest diagnostic ability.”
This is concerning for two reasons:
1. As I highlighted previously, k-fold cross-validation (CV) is purely a resampling method, not
a classifier or a machine learning algorithm. Describing it as a classifier is incorrect.
2. It remains unclear why SVM was used and what value it added to the analysis. Typically,
SVM is employed for tasks such as feature selection or ranking. However, the features in this
study appear limited to three biomarkers and their combination. In such cases, the standard
approach would be to calculate the AUC, derive confidence intervals using resampling, and
compare discrimination abilities via the DeLong’s test. Given this, I am unable to understand
the role of SVM in this context.
Further clarification on these points would be beneficial.
Title: Efficient diagnosis for endoscopic remission in Crohn’s diseases by the combination of
three non-invasive markers
Version: 3 Date: 31 Jan 2025
Reviewer's report:
I thank the reviewers for addressing the previous comments by amending their manuscript. The
methods and results sections are now clearer, which will enhance readers' acceptance of the
article.
However, I still find the following text problematic:
“Two different classifiers were adopted: k-fold cross-validation as a resampling method and a
support vector machine (SVM) to validate the diagnostic ability of multiple markers. Regarding
k-fold cross-validation, we performed cross-validation from 2-fold to 10-fold on each subject's
data and adopted the k value with the highest diagnostic ability.”
This is concerning for two reasons:
1. As I highlighted previously, k-fold cross-validation (CV) is purely a resampling method, not
a classifier or a machine learning algorithm. Describing it as a classifier is incorrect.
2. It remains unclear why SVM was used and what value it added to the analysis. Typically,
SVM is employed for tasks such as feature selection or ranking. However, the features in this
study appear limited to three biomarkers and their combination. In such cases, the standard
approach would be to calculate the AUC, derive confidence intervals using resampling, and
compare discrimination abilities via the DeLong’s test. Given this, I am unable to understand
the role of SVM in this context.
Further clarification on these points would be beneficial.
Please review this paper.
user
Title: Efficient diagnosis for endoscopic remission in Crohn’s diseases by the combination of
three non-invasive markers
Version: 3 Date: 31 Jan 2025
Reviewer's report:
I thank the reviewers for addressing the previous comments by amending their manuscript. The
methods and results sections are now clearer, which will enhance readers' acceptance of the
article.
However, I still find the following text problematic:
“Two different classifiers were adopted: k-fold cross-validation as a resampling method and a
support vector machine (SVM) to validate the diagnostic ability of multiple markers. Regarding
k-fold cross-validation, we performed cross-validation from 2-fold to 10-fold on each subject's
data and adopted the k value with the highest diagnostic ability.”
This is concerning for two reasons:
1. As I highlighted previously, k-fold cross-validation (CV) is purely a resampling method, not
a classifier or a machine learning algorithm. Describing it as a classifier is incorrect.
2. It remains unclear why SVM was used and what value it added to the analysis. Typically,
SVM is employed for tasks such as feature selection or ranking. However, the features in this
study appear limited to three biomarkers and their combination. In such cases, the standard
approach would be to calculate the AUC, derive confidence intervals using resampling, and
compare discrimination abilities via the DeLong’s test. Given this, I am unable to understand
the role of SVM in this context.
Further clarification on these points would be beneficial.
assistant