Description

Generates a complete Methods + Results report from your entropy weighting, TOPSIS ranking, and ML classifier outputs (XGBoost, Random Forest, Logistic Regression, etc.). It correctly interprets AUC using Hosmer & Lemeshow criteria and clearly flags the risk of overfitting when the training and test performance gap is large. It also anticipates at least four objections commonly raised by journal reviewers (e.g., “How was class imbalance addressed?” and “Why was no external validation set used?”) and provides a defense statement for each. Suitable for researchers working in health informatics, clinical risk prediction, or hybrid MCDA-ML methodologies, and for those seeking methodological rigor before journal submission.