КФУ. Карточка публикации. Assessment of healthy life years factors across European countries based on neural networks analysis.

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ASSESSMENT OF HEALTHY LIFE YEARS FACTORS ACROSS EUROPEAN COUNTRIES BASED ON NEURAL NETWORKS ANALYSIS.

Форма представления

Статьи в зарубежных журналах и сборниках

Год публикации

2025

Язык

английский

Авторы, сотрудники КФУ

Киршин Игорь Александрович, автор

Библиографическое описание на языке оригинала

Kirshin I. Assessment of healthy life years factors across European countries based on neural networks analysis. International Journal of Computational Economics and Econometrics. 2025. Vol.15. No.4. pp. 333-367. DOI:10.1504/IJCEE.2025.150005

Аннотация

The objective of this paper is to identify, test and evaluate the influence of health and disability factors on the healthy life years. Panel data from the Eurostat European Health Survey and Health Statistics covering 31 European countries from 2011 to 2022 were used to examine how healthy life years are associated with health and disability factors. A cross-country multiple regression analysis with dummy variables for the COVID-19 period was performed using the multiple linear regression model and the multilayer perceptron neural network in two versions: regression and time series (regression). The results obtained convincingly confirm the proposed hypothesis: healthy life years were significantly associated with self-assessed disability level and self-assessed long-term limitations in usual activities due to health problems, and to a lesser extent with share of people with good or very good perceived health and people with long-term diseases or health problems. Global sensitivity analysis showed that all networks determine the level of disability variable to be the most important. To test the robustness of the model, the random forest model was applied. The identified factors can be used as significant predictors of healthy life years assessment for European countries population.

Ключевые слова

healthy life years; time series analysis and forecasting; multiple linear regression analysis; neural networks; global sensitivity analysis; health inequalities; self-reported health

Название журнала

International Journal of Computational Economics and Econometrics

URL

https://www.inderscience.com/info/inarticle.php?artid=150005

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Полная запись метаданных

Поле DC	Значение	Язык
dc.contributor.author	Киршин Игорь Александрович	ru_RU
dc.date.accessioned	2025-01-01T00:00:00Z	ru_RU
dc.date.available	2025-01-01T00:00:00Z	ru_RU
dc.date.issued	2025	ru_RU
dc.identifier.citation	Kirshin I. Assessment of healthy life years factors across European countries based on neural networks analysis. International Journal of Computational Economics and Econometrics. 2025. Vol.15. No.4. pp. 333-367. DOI:10.1504/IJCEE.2025.150005	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/?p_id=320267	ru_RU
dc.description.abstract	International Journal of Computational Economics and Econometrics	ru_RU
dc.description.abstract	The objective of this paper is to identify, test and evaluate the influence of health and disability factors on the healthy life years. Panel data from the Eurostat European Health Survey and Health Statistics covering 31 European countries from 2011 to 2022 were used to examine how healthy life years are associated with health and disability factors. A cross-country multiple regression analysis with dummy variables for the COVID-19 period was performed using the multiple linear regression model and the multilayer perceptron neural network in two versions: regression and time series (regression). The results obtained convincingly confirm the proposed hypothesis: healthy life years were significantly associated with self-assessed disability level and self-assessed long-term limitations in usual activities due to health problems, and to a lesser extent with share of people with good or very good perceived health and people with long-term diseases or health problems. Global sensitivity analysis showed that all networks determine the level of disability variable to be the most important. To test the robustness of the model, the random forest model was applied. The identified factors can be used as significant predictors of healthy life years assessment for European countries population.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject		ru_RU
dc.title	Assessment of healthy life years factors across European countries based on neural networks analysis.	ru_RU
dc.type	Статьи в зарубежных журналах и сборниках	ru_RU