Predictive and Explainable Machine Learning Models for Endocrine, Nutritional, and Metabolic Mortality in Italy Using Geolocalized Pollution Data

Predictive and Explainable Machine Learning Models for Endocrine, Nutritional, and Metabolic Mortality in Italy Using Geolocalized Pollution Data

This study investigated the predictive performance of three regression models—Gradient Boosting (GB), Random Forest (RF), and XGBoost—in forecasting mortality due to endocrine, nutritional, and metabolic diseases across Italian provinces. Utilizing a dataset encompassing air pollution metrics and so...

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Bibliographic Details
Main Authors: Donato Romano, Michele Magarelli, Pierfrancesco Novielli, Domenico Diacono, Pierpaolo Di Bitonto, Nicola Amoroso, Alfonso Monaco, Roberto Bellotti, Sabina Tangaro
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Applied System Innovation
Subjects:
remote sensing
machine learning
nutritional disease
endocrine disease
metabolic disease
exposome
Online Access:https://www.mdpi.com/2571-5577/8/2/48
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