## 1 Introduction  
Currently, the world faces a considerable health burden related to tuberculosis (TB), which is an infectious bacterial disease caused by _Mycobacterium tuberculosis_, typically exerting adverse effects not only on the lungs, but also on other bodily organs. TB is transmitted from person to person via small droplets of sputum and saliva expelled when an infectious patient coughs or sneezes [1]. Declared a major worldwide health problem by the World Health Organization (WHO), TB induces ill-health among millions of people each year, and ranks as the second leading cause of death from infectious disease after human immunodeficiency virus (HIV) [2]. Nonetheless, TB is the most prevalent airborne infectious cause of death, inducing approximately three million deaths each year, principally among young adults in the globally poorest nations [3; 4; 5; 6; 7; 8; 9]. Smart cities have been paid attention, and its status consolidates as one of the fanciest areas of research today. Hence, [10] makes a case for a cautious rethink of the very rationale and relevance of the debate, and in the paper [11], the origins of what is termed normative bias in smart cities research are identified and a case is made for a holistic, scalable, and human-centered smart cities research agenda. Smart healthcare applications are one part of a smart city, which involve domain and data understanding for physician- and patient-centric healthcare, data preprocessing, and modeling using natural language processing and (big) data analytic techniques, and model evaluation and knowledge deployment through information infrastructures [12]. TB is often associated with behavioral factors and demographics, including occupation, age, tobacco and alcohol consumption, poor nutrition, and household crowding [13; 14; 15; 16; 17; 18]. Recently, WHO has begun to promote efforts to address social determinants as an important component of global tuberculosis control [19]. Recently, the improvement of medical conditions [20], the improvement of optimal control strategy [21], classification algorithm, and signal processing algorithm [22; 23], have been widely used in the medical field, meanwhile, big data and data analysis techniques are applied to disease diagnosis [24], such that the accuracy of diagnosis results has been significantly improved, and have contributed to preventing the incidence of tuberculosis diseases. Much of the epidemiological TB literature relies on notified cases, and relatively few involve measurements and trend predictions of TB prevalence [25]. However, the approaches related to the prediction of TB prevalence rates are less than ideal, and these possible tools deserve further exploration. Accurate tuberculosis prevalence rate forecasting is of vital importance to global tuberculosis prevention and control. Advances made in predicting tuberculosis events may be used to anticipate high and low risk years or future tuberculosis epidemics. In recent-year forecasts, future disease trends or comparisons of competing disease control policies commonly estimate results using dynamic transmission models, which represent the mechanisms of transmission, natural history, and health system interactions that generate tuberculosis outcomes. The studies shown in Table 1 described standard tuberculosis modeling approaches and examined specific modeling approaches. However, little systematic investigation has been done on the assumptions made by published tuberculosis models. If these assumptions are not valid, the results of these studies could be biased [26]. According to the above discussion, this paper seeks to use a combined model to estimate and forecast the prevalence of TB. We mainly focus on hierarchical tuberculosis prevalence rate data according to four World Bank income groups. The association between tuberculosis prevalence rates and income levels is examined by means of nonparametric analysis of variance (ANOVA).