Title: Factors influencing the incidence of early gastric cancer: A Bayesian Network analysis
Version: 2 Date: 25 Jan 2025
Reviewer:  Sajad Shafiekhani
Reviewer's report:
Dear Editor
The models implemented are not able properly predict both positive and negative cases because
of low sensitivity and specificity values, for example in SWSFS-BN method, the specificity
and sensitivity are 0.18 and 0.89 respectively and in Lasso-BN these values are 0.97 and
0.13 respectively. Maybe a method of voting between model predictions can use both high
sensitivity and high specificity power of different methods and generate an ensemble model with
high sensitivity and high specificity values. I recommend share data as supplementary file and
recommend in discussion for further analysis by interested researchers to do more analysis on
dataset. I recommend accept.
Title: Factors influencing the incidence of early gastric cancer: A Bayesian Network analysis
Version: 2 Date: 25 Jan 2025
Reviewer:  Sajad Shafiekhani
Reviewer's report:
Dear Editor
The models implemented are not able properly predict both positive and negative cases because
of low sensitivity and specificity values, for example in SWSFS-BN method, the specificity
and sensitivity are 0.18 and 0.89 respectively and in Lasso-BN these values are 0.97 and
0.13 respectively. Maybe a method of voting between model predictions can use both high
sensitivity and high specificity power of different methods and generate an ensemble model with
high sensitivity and high specificity values. I recommend share data as supplementary file and
recommend in discussion for further analysis by interested researchers to do more analysis on
dataset. I recommend accept.
Please review this paper.
user
Title: Factors influencing the incidence of early gastric cancer: A Bayesian Network analysis
Version: 2 Date: 25 Jan 2025
Reviewer:  Sajad Shafiekhani
Reviewer's report:
Dear Editor
The models implemented are not able properly predict both positive and negative cases because
of low sensitivity and specificity values, for example in SWSFS-BN method, the specificity
and sensitivity are 0.18 and 0.89 respectively and in Lasso-BN these values are 0.97 and
0.13 respectively. Maybe a method of voting between model predictions can use both high
sensitivity and high specificity power of different methods and generate an ensemble model with
high sensitivity and high specificity values. I recommend share data as supplementary file and
recommend in discussion for further analysis by interested researchers to do more analysis on
dataset. I recommend accept.
assistant