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Congratulations on the acceptance of the manuscript.
I reviewed the response to reviewers to check why reviewer #2 made this comment. Looking at their response, I agree that their explanation was sufficient such that edits did not need to be made to the manuscript.
No comment.
No comment.
No comment.
The authors have addressed all the comments.
Greetings, I can't find the corrections to the indicated observations, such as adding the confusion matrix of training and validation, as well as the explanation of why I use cv= 5 and not another value, etc.
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Please provide a revision, responding to those reviewers comments that you agree with in order to improve the quality of your submission.
Please refer to more recent references. It is recommended that the manuscript be thoroughly reviewed for grammar, punctuation, and sentence structure to enhance clarity and readability.
1. How can the model be implemented in clinical practice, and what are its advantages in a clinical setting?
2. How does the study intend to improve clinical decision-making in the context of CKD?
How were the 53 laboratory features selected, and could significant predictors have been excluded due to feature selection techniques?
3. What justifies the division of the dataset into a modelling dataset (n=296) and a validation dataset (n=71)? Is this sample size sufficient for robust model development and validation?
4. Did the study address the data imbalance issue, especially considering that only 148 out of 987 patients had kidney failure? How might this impact the performance of the model?
1. Is the decrease in AUC from the validation dataset (0.896) to the external dataset (0.771) concerning, and what does it reveal about the model’s stability and robustness?
2. What are the potential consequences if the model fails to predict CKD progression correctly, and how should clinicians mitigate such risks?
The resolution of the figure can be enhanced for better clarity and visual quality.
no comments
The article does not indicate what gap it is filling.
Draw a diagram to detail the procedure of the proposal.
Improve the conclusions in relation to the findings.
* In item 1:
Add 5 related studies as model assembly and likewise indicate how your proposed model differs.
*In item 2:
a) Make a diagram where you indicate the procedure of the experiment.
*In point 2.4
a) On what did you base your decision to use cross validation cv = 5?
b) Why didn't you do experiments with cv 5 to cv 10 in order to find the best model.
c) Also, could you indicate the parameters you used in each of the models.
*In point 4:
Make discussion of the results you ah obtained with respect to your background of point 1.
In figure 1:
*On which theories were based to perform the proposed Stacking with the XGBoost, LightGBM, Random Forest and logistic regression algorithms and not other algorithms.
*Place a correlation graph and determine the influence of each of them (variables) with the prediction of chronic kidney disease.
*To make a scaling of the variables to improve the result obtained.
*Place the confusion matrix obtained from both training and validation.
*Could indicate the practical and theoretical implications of the model.
*Indicate the limitations as well as the challenges and future work of the work.
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