Title: Factors influencing the incidence of early gastric cancer: A Bayesian Network analysis
Version: 1 Date: 24 Dec 2024
Reviewer:  Sajad Shafiekhani
Reviewer's report:
Dear Editor
I would like to thank you for passing this manuscript to me for review.
The manuscript is about prediction of gastric cancer by LASSO regression according to clinical
features. So many machine learning models can be used for determining which features are best
predictors of GC (feature selection methods such as MRMR, NCA,...) and so many classification
models can be used to classify GC from non-GC individuals. I propose to expand modeling
aspect of this study and compare and validate the results by comparing that with results of other
methods (such as methods I mentioned).
I recommend major revise.
I have some comments may help improve the manuscript:
1- introduction can be improved if pay attention to studies with aim to predict risk factors related
to gastric cancer and make a comprehensive report of their results.
2- In methods: Only Bayesian network was used to discriminate gastric cancer from non-gastric
cancer patients, while so many classification models such as SVM, KNN, Tree, Ensemble and
other can be implemented and outperformed Bayesian model of this study with .615 AUC. I
propose to use feature selection methods to find top scored predictors of GC and compare the
results with LASSO.
Title: Factors influencing the incidence of early gastric cancer: A Bayesian Network analysis
Version: 1 Date: 24 Dec 2024
Reviewer:  Sajad Shafiekhani
Reviewer's report:
Dear Editor
I would like to thank you for passing this manuscript to me for review.
The manuscript is about prediction of gastric cancer by LASSO regression according to clinical
features. So many machine learning models can be used for determining which features are best
predictors of GC (feature selection methods such as MRMR, NCA,...) and so many classification
models can be used to classify GC from non-GC individuals. I propose to expand modeling
aspect of this study and compare and validate the results by comparing that with results of other
methods (such as methods I mentioned).
I recommend major revise.
I have some comments may help improve the manuscript:
1- introduction can be improved if pay attention to studies with aim to predict risk factors related
to gastric cancer and make a comprehensive report of their results.
2- In methods: Only Bayesian network was used to discriminate gastric cancer from non-gastric
cancer patients, while so many classification models such as SVM, KNN, Tree, Ensemble and
other can be implemented and outperformed Bayesian model of this study with .615 AUC. I
propose to use feature selection methods to find top scored predictors of GC and compare the
results with LASSO.
Please review this paper.
user
Title: Factors influencing the incidence of early gastric cancer: A Bayesian Network analysis
Version: 1 Date: 24 Dec 2024
Reviewer:  Sajad Shafiekhani
Reviewer's report:
Dear Editor
I would like to thank you for passing this manuscript to me for review.
The manuscript is about prediction of gastric cancer by LASSO regression according to clinical
features. So many machine learning models can be used for determining which features are best
predictors of GC (feature selection methods such as MRMR, NCA,...) and so many classification
models can be used to classify GC from non-GC individuals. I propose to expand modeling
aspect of this study and compare and validate the results by comparing that with results of other
methods (such as methods I mentioned).
I recommend major revise.
I have some comments may help improve the manuscript:
1- introduction can be improved if pay attention to studies with aim to predict risk factors related
to gastric cancer and make a comprehensive report of their results.
2- In methods: Only Bayesian network was used to discriminate gastric cancer from non-gastric
cancer patients, while so many classification models such as SVM, KNN, Tree, Ensemble and
other can be implemented and outperformed Bayesian model of this study with .615 AUC. I
propose to use feature selection methods to find top scored predictors of GC and compare the
results with LASSO.
assistant