Climate change increasingly affects every aspect of human life. Recent studies report a close correlation with human health and it is estimated that global death rates will increase by 73 per 100,000 by 2100 due to changes in temperature. In this context, the present work aims to study the correlation between climate change and human health, on a global scale, using artificial intelligence techniques. Starting from previous studies on a smaller scale, that represent climate change and which at the same time can be linked to human health, four factors were chosen. Four causes of mortality, strongly correlated with the environment and climatic variability, were subsequently selected. Various analyses were carried out, using neural networks and machine learning to find a correlation between mortality due to certain diseases and the leading causes of climate change. Our findings suggest that anthropogenic climate change is strongly correlated with human health; some diseases are mainly related to risk factors while others require a more significant number of variables to derive a correlation. In addition, a forecast of victims related to climate change was formulated. The predicted scenario confirms that a prevalently increasing trend in climate change factors corresponds to an increase in victims.

Analysis of Correlation between Climate Change and Human Health Based on a Machine Learning Approach

Vito Telesca
;
2021-01-01

Abstract

Climate change increasingly affects every aspect of human life. Recent studies report a close correlation with human health and it is estimated that global death rates will increase by 73 per 100,000 by 2100 due to changes in temperature. In this context, the present work aims to study the correlation between climate change and human health, on a global scale, using artificial intelligence techniques. Starting from previous studies on a smaller scale, that represent climate change and which at the same time can be linked to human health, four factors were chosen. Four causes of mortality, strongly correlated with the environment and climatic variability, were subsequently selected. Various analyses were carried out, using neural networks and machine learning to find a correlation between mortality due to certain diseases and the leading causes of climate change. Our findings suggest that anthropogenic climate change is strongly correlated with human health; some diseases are mainly related to risk factors while others require a more significant number of variables to derive a correlation. In addition, a forecast of victims related to climate change was formulated. The predicted scenario confirms that a prevalently increasing trend in climate change factors corresponds to an increase in victims.
2021
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11563/148964
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