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 (percent 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 quality and performance of health care systems.
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