Abstract
In recent years, healthcare has attracted much attention, which is looking for more and more data analytics in healthcare to relieve medical problems in medical staff shortage, ageing population, people living alone, and quality of life. Data mining, analysis, and forecasting play a vital role in modern social and medical fields. However, how to select a proper model to mine and analyze the relevant medical information in the data is not only an extremely challenging problem, but also a concerning problem. Tuberculosis remains a major global health problem despite recent and continued progress in prevention and treatment. There is no doubt that the effective analysis and accurate forecasting of global tuberculosis prevalence rates lay a solid foundation for the construction of an epidemic disease warning and monitoring system from a global perspective. In this paper, the tuberculosis prevalence rate time series for four World Bank income groups are targeted. Kruskal–Wallis analysis of variance and multiple comparison tests are conducted to determine whether the differences of tuberculosis prevalence rates for different income groups are statistically significant or not, and a novel combined forecasting model with its weights optimized by a recently developed artificial intelligence algorithm—cuckoo search—is proposed to forecast the hierarchical tuberculosis prevalence rates from 2013 to 2016. Numerical results show that the developed combination model is not only simple, but is also able to satisfactorily approximate the actual tuberculosis prevalence rate, and can be an effective tool in mining and analyzing big data in the medical field.
Highlights
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 on the lungs, and on other bodily organs
One-third of the twelve forecasting values derived from the proposed combination forecasting model are exactly equal to their real values
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
Summary
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 on the lungs, and on other bodily organs. 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]. 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].
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