Abstract
Tuberculosis, as a more common infectious disease with serious physical damage to humans, has been relatively vacant in predictive model research. In order to improve the accuracy of pulmonary tuberculosis, this study combined the incidence of tuberculosis, collected data using data collection methods, used a single data model for predictive analysis, and compared with the actual situation. At the same time, through the comparative analysis, the paper draws the shortcomings of the traditional single model algorithm, constructs a combined model for the prediction of tuberculosis, and collects the incidence of tuberculosis. In addition, this paper draws it into a statistical chart, and analyzes its pathological characteristics and the dynamic trend of the onset. Through experimental research, it can be seen that the prediction accuracy of the combined model of this study is high, which can provide theoretical reference for subsequent related research.
Published Version
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