〔Abstract〕 Abstract Objective To explore the feasibility of machine learning methods in the discrimination of tuberculosis patients . Methods The data of 15 observation indicators of 860 patients were obtained from a tertiary hospital . Through in-depth mining and analysis of the data , support vector machine , random forest and neural network model methods were used to discriminate the diseases of patients . Results The accuracies of the TB suspected patient discrimination models based on support vector machine , random forest and neural network were 90% , 91% and 88% , respectively. Conclusion All three machine learning methods can be used for discriminative analysis of suspected tuberculosis patients . In comparison , random forest performs better in discriminating patients with tuber- culosis from those with pneumonia.