## 1 Introduction  
Non-communicable diseases, particularly endocrine, nutritional, and metabolic disorders, represent a growing public health concern worldwide. These conditions, which include diabetes, thyroid disorders, and malnutrition-related complications, are influenced by complex interactions between genetic, environmental, and socio-economic factors [1]. In Italy, substantial regional disparities in mortality rates from such diseases suggest a significant role for external determinants, including air pollution and socio-economic conditions [2]. The incidence of these disorders varies across age groups, with type 1 diabetes often emerging in childhood or adolescence, posing lifelong management challenges [3],while type 2 diabetes and thyroid dysfunctions predominantly affect middle-aged and elderly populations due to aging, lifestyle factors, and dietary habits [4]. Malnutrition-related complications, though less frequent in high-income countries, disproportionately impact vulnerable groups such as infants, the elderly, and socio-economically disadvantaged populations [5]. These conditions carry severe health consequences, including chronic complications like cardiovascular disease, kidney failure, cognitive impairments, and heightened susceptibility to infections, which significantly reduce quality of life and life expectancy [6]. Socio-economic and social costs are equally profound, covering direct expenses for medical care, diagnostics, and treatment, as well as indirect costs arising from productivity losses, absenteeism, early retirement, and financial strain on families and caregivers [7]. In Italy, diabetes management alone consumes a substantial portion of healthcare resources, highlighting the immense pressure these diseases place on public health systems [8]. Socio-economically disadvantaged areas bear a disproportionate burden, exacerbating health inequalities and underscoring the need for comprehensive public health strategies that integrate preventive measures, early detection programs, and equitable access to care and support systems [9]. Air pollution has been widely recognized as a major contributor to various health conditions, including endocrine and metabolic disorders. Studies have linked exposure to pollutants such as particulate matter (PM10, PM2.5), nitrogen oxides (NO and NO\({}_{2}\)), and ozone (O\({}_{3}\)) to systemic inflammation, hormonal disruptions, and metabolic imbalances [10; 11; 12]. Concurrently, socio-economic variables, such as income, education levels, healthcare access, and urbanization, are known to modulate health outcomes, acting as either risk amplifiers or protective buffers [13]. However, the interplay between these factors remains underexplored, particularly in the context of mortality associated with endocrine, nutritional, and metabolic diseases. The advent of machine learning (ML) offers powerful tools to analyze large, complex datasets and uncover hidden patterns that traditional statistical methods may overlook. ML models can integrate diverse data sources, ranging from environmental pollution metrics to socio-economic indicators, to predict health outcomes with a high accuracy [14]. Moreover, explainable artificial intelligence (XAI) frameworks, such as Shapley Additive Explanations (SHAP), enable the interpretation of model predictions, shedding light on the relative importance and interplay of contributing factors [15]. This combination of predictive capability and interpretability makes ML a valuable approach to addressing public health challenges [16; 17; 18; 19]. This study applied ML techniques to assess the likelihood of mortality from endocrine, nutritional, and metabolic diseases across Italian provincial units. By integrating air quality data and economic and social condition indices with mortality statistics obtained from ISTAT (Italian National Institute of Statistics), we aimed to identify key predictors and quantify their contributions to health outcomes.