Analysis of life expectancy across countries using a decision tree

Abstract

Background: It is important to identify variables that influence life expectancy in order to develop strategies to improve health care systems and thereby increase life expectancy. Aims: In this study, a decision tree was built using a chi-square automatic interaction detector technique in order to identify variables influencing life expectancy at birth. Methods: Data were taken from the databases of the World Bank, World Health Organization and World Life Expectancy. Data from 166 countries for the year 2013 were extracted for 25 selected input variables related to mortality, health and the environment, child health, economy and demography in order to build the decision tree. Results: Of the 25 variables, nine had a significant influence on life expectancy: percentage of the population using improved sanitation facilities; death rates per 100 000 population for HIV/AIDS, liver disease, stroke and coronary heart disease; percentage of the urban population using improved drinking-water sources; total fertility rate (births per woman); public health expenditure (per cent of government expenditure); and health expenditure per capita. Conclusions: Improving these variables may result in significant increases in life expectancy and quality of life. At the country level, appropriate strategies can be developed to improve the 1 / 10 WHO EMRO | Analysis of life expectancy across countries using a decision tree quality and performance of health care systems.

A Cognitive Analysis of Colors on Products and Brands

“…Improving quality in these variables may result in increases in life expectancy and quality of life. Based on these, appropriate strategies can be developed to improve the quality and performance of health care systems by each country [10].…”

Pollution and probabilistic human health risk assessment of potentially toxic elements in the soil-water-plant system in the Bolkar mining district, Niğde, south-central Turkey

“…The carcinogenic risk (CR) is expressed in terms of the probability that one is likely to have cancer owing to the intake of arsenic-contaminated water during a lifetime exposure of approximately 77 years, since that is the life expectancy age for people living in Turkey (Karacan et al 2020). The CR of being exposed to As by the drinking water is in the range of 0.00002 to 0.00540 in the winter season (Table 5).…”

“…(2 L/day) taken from (USEPA 2004), ED is the exposure duration (years) to the PTEs and according to the archives of the USEPA (USEPA 1997), 30 years is used, EF denotes the exposure frequency (days/year) taken as 365 days/year (USEPA 2004), BW is the average body weight (kg) (61.75) fromÇiner et al (2020), AT is the average time of exposure (d) and in this study, for the non-carcinogenic risk, AT = ED × 365 and for carcinogenic risk, AT = 77.434 × 365 since 77.434 is the average life expectancy of people living in Turkey(Karacan et al 2020). …”