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Machine learning techniques employed to formulate diagnostic models for NASH. A The model built with the RF algorithm demonstrated the highest predictive accuracy, boasting a C-index value of 0.928. B-I The AUC values of the three model genes for the separate diagnosis of NASH in both the training set and the external validation set were relatively high
