Data Analysis and Forecasting of Tuberculosis Prevalence Rates for Smart Healthcare Based on a Novel Combination Model

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.

eGAP: An Evolutionary Game Theoretic Approach to Random Forest Pruning

“…The authors in [12] investigated the prediction of biochemical recurrences in patients treated by stereotactic body radiation therapy using prostate clinical outlook. Using Kruskal-Wallist test, regression model, Cuckoo search optimisation algorithm, and radial basis function neural network, a model was developed in [13] to determine whether the differences of tuberculosis prevalence rates for different income groups are statistically significant or not.…”

Data Analytics in Smart Healthcare: The Recent Developments and Beyond

“…The manuscript "Data analysis and forecasting of tuberculosis prevalence rates for smart healthcare based on a novel combination model" authored by J. Wang, C. Wang and W. Zhang proposed a combination forecasting model to determine the tuberculosis prevalence rate [10]. The steps have been divided into five major parts, including the Kruskal-Wallist test (also known as one-way analysis of variance (ANOVA) on ranks), regression model, cuckoo search optimization algorithm, combine forecasting method with weighted coefficients, and radial basis function neural networks.…”

How Big Data in Health 4.0 Helps Prevent the Spread of Tuberculosis

“…the value that is referred to is based on the income groups of the population in the whole world and the results obtained by the prevention level are very satisfying. [8] [9]. The use of big data in the health area has been very popular nowadays especially for era industry 4.0 in the health area, such as in developed countries using big data to monitor patients health by implanting biosensors [10] [11].…”

“…like TABLE II below about medical data on the disease. Some of the Big Data that can be taken as open source as in the World WHO Institute for handling HIV prevention, Tuberculosis globally [8]. But some literature explains the use of data taken from hospitals [9][19] [25], good for monitoring purposes.…”