[The application of machine learning in tuberculosis surveillance, early warning, and evaluation of intervention strategies].

Abstract

As one of the major public health challenges globally, tuberculosis requires epidemiological research for its control and prevention. With the advent of the big data era, machine learning has advantages over traditional methods in handling complex, high-dimensional datasets and providing accurate predictive results. This paper introduces the application of machine learning in the discovery and diagnosis of tuberculosis cases, risk factor analysis, predictive modeling, and evaluation of intervention strategies, providing new means for more in-depth exploration of the value in tuberculosis epidemiological research.