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    Predictive and Explainable Machine Learning Models for Endocrine, Nutritional, and Metabolic Mortality in Italy Using Geolocalized Pollution Data by Donato Romano, Michele Magarelli, Pierfrancesco Novielli, Domenico Diacono, Pierpaolo Di Bitonto, Nicola Amoroso, Alfonso Monaco, Roberto Bellotti, Sabina Tangaro

    Published 2025-04-01
    “…Performance was assessed using metrics such as coefficient of determination (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>r</mi><mn>2</mn></msup></semantics></math></inline-formula>), mean absolute error (MAE), and root mean squared error (RMSE), revealing that GB outperformed both RF and XGB, offering superior predictive accuracy and model stability (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>r</mi><mn>2</mn></msup></semantics></math></inline-formula> = 0.55, MAE = 0.17, and RMSE = 0.05). …”
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