<![CDATA[This study investigates key socioeconomic determinants of life expectancy (LE) and develops a regional-level predictive model using the Random Forest regression approach for regencies and municipalities in Central Kalimantan Province during 2016–2023. Although life expectancy is a core indicator of human development, empirical studies employing machine learning methods at the sub-provincial level in Indonesia remain limited. Using secondary data from Statistics Indonesia (BPS), this study examines the relationship between LE and selected indicators related to education, sanitation, health infrastructure, economic conditions, and demography. The Random Forest model exhibits robust predictive performance, achieving MAE values of approximately 0.29–0.30 and coefficients of determination (R²) ranging from 0.71 to 0.74 across different evaluation schemes. Feature importance analysis identifies mean years of schooling as the most influential determinant of life expectancy, followed by access to proper sanitation and the availability of health facilities. These results highlight the prominent role of human capital and basic infrastructure in shaping regional health outcomes. By integrating machine learning techniques with regional socioeconomic data, this study extends existing life expectancy research in Indonesia through a data-driven modeling framework. Overall, this study supports evidence-based planning by highlighting priority intervention areas to improve life expectancy and human development in Central Kalimantan.]]>
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