## 1 Introduction
In addition, nonlinear regression analysis is first applied to hierarchically forecast tuberculosis prevalence rates; then, a combination forecasting strategy, whose weights are further optimized by the cuckoo search algorithm, based on machine learning, is proposed. Cuckoo search-based combined models are constructed in this paper to improve forecasting accuracy as much as possible and, thus, provide meaningful evidence and information about the potential trends and future evaluation of the burden of tuberculosis, i.e., incidence, prevalence, and mortality. In conclusion, the major distinction of this study is that hierarchical tuberculosis prevalence rates are innovatively analyzed and forecasted. Furthermore, an innovative combination forecasting model based on regression analysis and an artificial intelligence optimization method is proposed. In the future, big data and data analysis technology will be widely used in disease surveillance, decision-making, health management, and other fields, which is the focus of current intelligent medical care. In this paper, data analysis is used to analyze and forecast the tuberculosis prevalence rates. Through repeated analysis of tuberculosis data, combined with the data of tuberculosis prevalence rates and professional literature, a hybrid combined forecasting model is proposed, verified repeatedly and, finally, the CS-combined model is used to forecasting the trend of prevalence rates of intelligent medical products. \begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**Reference**} & \multicolumn{1}{c}{**Description**} & \multicolumn{1}{c}{**Model**} \\ \hline Exogenous re-infection and the dynamics of tuberculosis epidemics: local effects in a network model of transmission & A network model of TB transmission to evaluate the impact of non-homogeneous mixing on the relative contribution of re-infection over realistic epidemic & Mathematical models \\ \hline The impact of realistic age structure in simple models of tuberculosis transmission & A simple model of TB transmission, with alternative assumptions about survivorship, is used to explore the effect of age structure on the prevalence of infection, disease, basic reproductive ratio, and the projected impact of control interventions [28] & Mathematical models \\ \hline Appropriate models for the management of infectious diseases. & The model intrinsic assumptions embedded within classical frameworks [29] & Mathematical models \\ \hline Forecast analysis of the incidence of tuberculosis in the province of Quebec & A compartmental differential equation based on a susceptible exposed latent infectious recovered (SELIR) model was simulated using the Euler method [30] & Mathematical models \\ \hline On the role of variable latent periods in mathematical models for tuberculosis & The model that combine with arbitrarily distributed latent stage are similar to those given by the TB model with an exponentially distributed period of latency [31] & mathematical models \\ \hline Emergent heterogeneity in declining tuberculosis epidemics & Using two mathematical models to explore the role of the contact structure of the population, and find that in declining epidemics, localized outbreaks may occur as a result of contact heterogeneity, even in the absence of host or strain variability [32] & mathematical models \\ \hline Epidemiological models of _Mycobacterium tuberculosis_ complex infections & Epidemiological models & Epidemiological models \\ \hline Mathematical modeling of the epidemiology of tuberculosis & This is reflected in differences in the structures of mathematical models of TB which, in turn, produce differences in the predicted impacts of interventions. Gaining a greater understanding of TB transmission dynamics requires further empirical laboratory and field work, mathematical modeling, and interaction between them [34] & Mathematical Modeling \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The different forecasting approaches of tuberculosis (TB).