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Lifespan Prediction Using Socio-Economic Data Using Machine Learning

Lifespan Prediction Using Socio-Economic Data Using Machine Learning

Veysel Gökhan Aydin, Elif Bulut
Copyright: © 2022 |Pages: 23
ISBN13: 9781668440452|ISBN10: 1668440458|ISBN13 Softcover: 9781668440469|EISBN13: 9781668440476
DOI: 10.4018/978-1-6684-4045-2.ch002
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MLA

Aydin, Veysel Gökhan, and Elif Bulut. "Lifespan Prediction Using Socio-Economic Data Using Machine Learning." Machine Learning for Societal Improvement, Modernization, and Progress, edited by Vishnu S. Pendyala, IGI Global, 2022, pp. 27-49. https://doi.org/10.4018/978-1-6684-4045-2.ch002

APA

Aydin, V. G. & Bulut, E. (2022). Lifespan Prediction Using Socio-Economic Data Using Machine Learning. In V. Pendyala (Ed.), Machine Learning for Societal Improvement, Modernization, and Progress (pp. 27-49). IGI Global. https://doi.org/10.4018/978-1-6684-4045-2.ch002

Chicago

Aydin, Veysel Gökhan, and Elif Bulut. "Lifespan Prediction Using Socio-Economic Data Using Machine Learning." In Machine Learning for Societal Improvement, Modernization, and Progress, edited by Vishnu S. Pendyala, 27-49. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-4045-2.ch002

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Abstract

Average life expectancy may change among different regions within the same society as well as among countries. In this study, a multiple linear regression model and a support vector regression model were established by addressing some economic and social variables of the countries. The data of 32 countries for the years 2017 and 2018 was compiled within the scope of the study, and it was attempted to determine which model was better. The aim of this study is to compare the prediction performances of support vector regression and multiple linear regression analyses. Support vector regression analysis was applied by the use of radial basis functions, linear, polynomial, and sigmoid kernel functions. In addition, the multiple linear regression analysis method was also applied using the least squares method, and the results were compared. For the comparison of the results, error bound accuracy rates were calculated, and the comparison was made according to these rates. The predictions were also examined through graphical methods, and it was attempted to determine the best model.

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