<![CDATA[Dengue Hemorrhagic Fever (DHF) control strategies in urban Indonesia often rely on uniform interventions that fail to account for the spatial heterogeneity of disease outcomes. While Incidence Rate (IR) is commonly used to map risk, it overlooks the clinical severity represented by the Case Fatality Rate (CFR). This study creates a novel spatial epidemiological typology by integrating both IR and CFR using an unsupervised machine learning approach. analyzing data from 16 sub-districts in Semarang City (2016–2024), we constructed a dual-indicator clustering model. The analysis reveals three distinct risk typologies: (1) High Transmission Zones (High IR), driven by population density; (2) High Mortality Zones (High CFR, Low IR), indicating "silent" risks and potential clinical management gaps; and (3) Controlled Zones. Unlike traditional single-indicator mapping, this proposed typology offers a precise, data-driven framework for decision-makers, enabling the separation of vector control priorities from clinical system strengthening. ]]>
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