## 4 Conclusions  
Concerning the association of income status and prevalence rate, a non-parametric Kruskal-Wallis test is performed, and the matrix derived from the test demonstrates that there are significant differences in tuberculosis prevalence rates among pairwise income groups, except between the lower-middle income and the low income group. In addition, individual regression models are constructed to fit the tuberculosis prevalence rates from 1999 to 2012 for the four income groups. The quadratic polynomial model, the two-term exponential model, the sum-of-sines model, and the Gaussian model, are repeatedly used to forecast the tuberculosis prevalence rates from 2013 to 2016, with two types of variable transformations: taking reciprocals and base-2 logarithms. All selected individual regression models have satisfactory goodness-of-fit with adjusted \(R\)-squares all greater than 0.96. Combined forecasting models are proposed based on six individual regression models, and the weights are optimized by the cuckoo search algorithm, which is based on machine learning. From the extensive simulation results, it can be concluded that for each of the four income groups, the proposed combination forecasting models based on artificial intelligence optimization always provide better forecast accuracy than  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & & & \\ \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Forecasting**} & \multirow{2}{*}{**Poly2**} & \multirow{2}{*}{**Sin2**} & \multirow{2}{*}{**Reci-ploy2**} & \multirow{2}{*}{**Reci-exp2**} & \multirow{2}{*}{**Power2-ploy2**} & \multirow{2}{*}{**Power2-Exp2**} & \multirow{2}{*}{**CS-Combined**} \\  & & & & & & & & & \\ \hline \multirow{2}{*}{**Training**} & **1-order** & 0.999509 & 0.999423 & 0.998975 & 0.99945 & 0.999345 & 0.999273 & 0.999628 \\  & **1-order** & 0.998405 & 0.99863 & 0.99874 & 0.998651 & 0.998815 & 0.998572 & 0.99945 \\  & **2-order** & 0.999509 & 0.999423 & 0.999875 & 0.999449 & 0.999344 & 0.999272 & 0.999628 \\  & **2-order** & 0.998403 & 0.998662 & 0.99874 & 0.99865 & 0.998814 & 0.998571 & 0.999445 \\ \hline \multicolumn{8}{c}{**Lower-Middle Income Group**} \\ \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Forecasting**} & \multirow{2}{*}{**Poly2**} & \multirow{2}{*}{**Sin1**} & \multirow{2}{*}{**Reci-ploy2**} & \multirow{2}{*}{**Reci-exp2**} & \multirow{2}{*}{**Power2-ploy2**} & \multirow{2}{*}{**Power2-Exp2**} & \multirow{2}{*}{**CS-Combined**} \\  & & & & & & & & \\ \hline \multirow{2}{*}{**Training**} & **1-order** & 0.999022 & 0.999068 & 0.999376 & 0.999568 & 0.999179 & 0.999679 & 0.999819 \\  & **1-order** & 0.99901 & 0.99909 & 0.999311 & 0.999789 & 0.999234 & 0.999771 & 0.999818 \\  & **2-order** & 0.999022 & 0.999068 & 0.999376 & 0.999568 & 0.999178 & 0.999679 & 0.99819 \\  & **2-order** & 0.999041 & 0.99909 & 0.999311 & 0.999789 & 0.999233 & 0.999771 & 0.999818 \\ \hline \multicolumn{8}{c}{**Upper-Middle Income Group**} \\ \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Forecasting**} & \multirow{2}{*}{**Poly2**} & \multirow{2}{*}{**Sin1**} & \multirow{2}{*}{**Reci-ploy2**} & \multirow{2}{*}{**Reci-exp2**} & \multirow{2}{*}{**Power2-ploy2**} & \multirow{2}{*}{**Power2-Exp2**} & \multirow{2}{*}{**CS-combined**} \\  & & & & & & & & \\ \hline \multirow{2}{*}{**Training**} & **1-order** & 0.998723 & 0.99884 & 0.999066 & 0.99883 & 0.999056 & 0.998964 & 0.998958 \\  & **1-order** & 0.997559 & 0.997689 & 0.99857 & 0.998418 & 0.998063 & 0.998609 & 0.999496 \\  & **2-order** & 0.998722 & 0.998839 & 0.999066 & 0.99892 & 0.9990 the individual regression models. As a result, these findings provide substantial information about the effectiveness and stability of the proposed combination forecasting model in the forecasting of hierarchical tuberculosis prevalence rates. Future healthcare is research on the interaction between patient-centered healthcare and all pillar industries, which uses data science to store, capture, and mine the relationship between medical data and patients.