Syndrome Classification of Chronic Gastritis Based on Extremely Randomized Forest Algorithm

Syndrome differentiation and treatment,which is the essence of traditional Chinese medicine (TCM),contain abundant rules.The majority of machine learning algorithms can obtain good classification accuracy,but these models are difficult to be explained.The models established by random forests have great interpretability,while these models cannot deal with multi-syndrome that patients may simultaneously have more than one syndrome in TCM.In this paper,syndrome classification for Chronic Gastritis (CG) is researched by using extremely randomized forest (ERF) algorithm,and compared with state-of-the-art multi-label algorithms and the tree-based algorithms (such as C4.5,CART).The experimental results show that ERF algorithm has better performance than other algorithms in the classification accuracy of every label and the six evaluation metrics of multi-label learning.The rules obtained in the model are basically in accord with TCM theory.