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 tuberculosis from those with pneumonia.

Artificial intelligence-based medical decision-making systems can significantly accelerate decision processes and enhance accuracy.However,challenges persist in achieving personalized care and in the compre-hensive collection of medical data.This paper explores potential solutions to these issues by examining AI ap-plications in diagnostic omics and multi-dimensional data acquisition,providing an overview of current pro-gress and limitations in developing intelligent medical decision systems through these approaches.Additional-ly,the paper also discusses the broad potential for artificial intelligence applications in medical education and their possible contributions to advancing overall decision-making standards in healthcare.