Title: Risk prediction and effect evaluation of complicated appendicitis based on XGBoost
modeling
Version: 0 Date: 01 Mar 2025
Reviewer:  Miguel Puga-Tejada
Reviewer's report:
Contratulations. This is a great research. The study presents an innovative application of
machine learning to differentiate complicated from uncomplicated appendicitis, with notable
performance of the proposed model. The manuscript is well written and illustrated.
Title: Risk prediction and effect evaluation of complicated appendicitis based on XGBoost
modeling
Version: 0 Date: 01 Mar 2025
Reviewer:  Miguel Puga-Tejada
Reviewer's report:
Contratulations. This is a great research. The study presents an innovative application of
machine learning to differentiate complicated from uncomplicated appendicitis, with notable
performance of the proposed model. The manuscript is well written and illustrated.
Please review this paper.
user
Title: Risk prediction and effect evaluation of complicated appendicitis based on XGBoost
modeling
Version: 0 Date: 01 Mar 2025
Reviewer:  Miguel Puga-Tejada
Reviewer's report:
Contratulations. This is a great research. The study presents an innovative application of
machine learning to differentiate complicated from uncomplicated appendicitis, with notable
performance of the proposed model. The manuscript is well written and illustrated.
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