Title: Risk prediction and effect evaluation of complicated appendicitis based on XGBoost
modeling
Version: 0 Date: 24 Feb 2025
Reviewer's report:
I have reviewed your manuscript. My suggestions are as follows.
Ensure consistent use of terms throughout the section. For example, "Xgboost" should always
be written as "XGBoost," and "Svm" should be "SVM."
In scientific writing, it is generally recommended to spell out numbers less than ten (e.g., "five
to six percent" instead of "5–6%"). This maintains a formal tone.
The final sentence could be stronger. Instead of just stating the aim of the study, consider briefly
outlining the significance of this research and how it addresses the identified gap in current
diagnostic practices.
The exclusion criteria could be more explicitly stated. For example, clarify what constitutes
"significant outliers" and how "missing data" was handled.
While you provide hyperparameters for the models, consider adding a brief rationale for why
those specific values were chosen. For example, how were these values determined (e.g., grid
search, prior studies)?
Clarify that "SVM" stands for Support Vector Machine when first mentioned. Additionally,
explain what "cost" and "gamma" mean in practical terms for the model's performance.
When discussing sensitivity, specificity, and precision, briefly explain what these metrics mean
in the context of clinical decision-making.
When discussing the performance of other models (e.g., ANN, CatBoost), provide a brief
explanation of why your approach (without radiological imaging) is advantageous.
Title: Risk prediction and effect evaluation of complicated appendicitis based on XGBoost
modeling
Version: 0 Date: 24 Feb 2025
Reviewer's report:
I have reviewed your manuscript. My suggestions are as follows.
Ensure consistent use of terms throughout the section. For example, "Xgboost" should always
be written as "XGBoost," and "Svm" should be "SVM."
In scientific writing, it is generally recommended to spell out numbers less than ten (e.g., "five
to six percent" instead of "5–6%"). This maintains a formal tone.
The final sentence could be stronger. Instead of just stating the aim of the study, consider briefly
outlining the significance of this research and how it addresses the identified gap in current
diagnostic practices.
The exclusion criteria could be more explicitly stated. For example, clarify what constitutes
"significant outliers" and how "missing data" was handled.
While you provide hyperparameters for the models, consider adding a brief rationale for why
those specific values were chosen. For example, how were these values determined (e.g., grid
search, prior studies)?
Clarify that "SVM" stands for Support Vector Machine when first mentioned. Additionally,
explain what "cost" and "gamma" mean in practical terms for the model's performance.
When discussing sensitivity, specificity, and precision, briefly explain what these metrics mean
in the context of clinical decision-making.
When discussing the performance of other models (e.g., ANN, CatBoost), provide a brief
explanation of why your approach (without radiological imaging) is advantageous.
Please review this paper.
user
Title: Risk prediction and effect evaluation of complicated appendicitis based on XGBoost
modeling
Version: 0 Date: 24 Feb 2025
Reviewer's report:
I have reviewed your manuscript. My suggestions are as follows.
Ensure consistent use of terms throughout the section. For example, "Xgboost" should always
be written as "XGBoost," and "Svm" should be "SVM."
In scientific writing, it is generally recommended to spell out numbers less than ten (e.g., "five
to six percent" instead of "5–6%"). This maintains a formal tone.
The final sentence could be stronger. Instead of just stating the aim of the study, consider briefly
outlining the significance of this research and how it addresses the identified gap in current
diagnostic practices.
The exclusion criteria could be more explicitly stated. For example, clarify what constitutes
"significant outliers" and how "missing data" was handled.
While you provide hyperparameters for the models, consider adding a brief rationale for why
those specific values were chosen. For example, how were these values determined (e.g., grid
search, prior studies)?
Clarify that "SVM" stands for Support Vector Machine when first mentioned. Additionally,
explain what "cost" and "gamma" mean in practical terms for the model's performance.
When discussing sensitivity, specificity, and precision, briefly explain what these metrics mean
in the context of clinical decision-making.
When discussing the performance of other models (e.g., ANN, CatBoost), provide a brief
explanation of why your approach (without radiological imaging) is advantageous.
assistant