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Insights from Explainable Artificial Intelligence of Pollution and Socioeconomic Influences for Respiratory Cancer Mortality in Italy.

Authors :
Romano, Donato
Novielli, Pierfrancesco
Diacono, Domenico
Cilli, Roberto
Pantaleo, Ester
Amoroso, Nicola
Bellantuono, Loredana
Monaco, Alfonso
Bellotti, Roberto
Tangaro, Sabina
Source :
Journal of Personalized Medicine; Apr2024, Vol. 14 Issue 4, p430, 11p
Publication Year :
2024

Abstract

Respiratory malignancies, encompassing cancers affecting the lungs, the trachea, and the bronchi, pose a significant and dynamic public health challenge. Given that air pollution stands as a significant contributor to the onset of these ailments, discerning the most detrimental agents becomes imperative for crafting policies aimed at mitigating exposure. This study advocates for the utilization of explainable artificial intelligence (XAI) methodologies, leveraging remote sensing data, to ascertain the primary influencers on the prediction of standard mortality rates (SMRs) attributable to respiratory cancer across Italian provinces, utilizing both environmental and socioeconomic data. By scrutinizing thirteen distinct machine learning algorithms, we endeavor to pinpoint the most accurate model for categorizing Italian provinces as either above or below the national average SMR value for respiratory cancer. Furthermore, employing XAI techniques, we delineate the salient factors crucial in predicting the two classes of SMR. Through our machine learning scrutiny, we illuminate the environmental and socioeconomic factors pertinent to mortality in this disease category, thereby offering a roadmap for prioritizing interventions aimed at mitigating risk factors. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20754426
Volume :
14
Issue :
4
Database :
Complementary Index
Journal :
Journal of Personalized Medicine
Publication Type :
Academic Journal
Accession number :
176875023
Full Text :
https://doi.org/10.3390/jpm14040430