Substantive Interpretation of Machine Learning Solutions by the Example of Determining the Activity of the Tuberculosis Process in Individuals with Minimal Tuberculosis Residual Changes

In this article is described an application of various machine learning (ML) methods to obtain decision rules and its interpretation to a problem of recognition of activity of the tuberculosis process. The research data base included 489 patients registered in anti-tuberculosis institutions in Tyumen and Yekaterinburg. The conducted modeling by machine learning methods allowed to highlight 7 most informative features (the presence of calcifications, age, the content of leukocytes, hemoglobin, eosinophils, α2-fraction of globulins, γ-fraction of globulins) together with classification accuracy of 95% for both active and inactive patients. The research result may be interesting for medical specialists, data scientists and to all those interested in problems at the intersection of medicine and machine learning.