<![CDATA[Diabetes mellitus (DM) is a growing metabolic and autoimmune-related disease whose early onset is increasingly observed among young adults, including the university students in Indonesia. The existing screening models are either costly, invasive, or fail to integrate lifestyle data, leaving a gap for practical yet scalable solutions in this population. This study introduces a smart screening technology that combines the Food Frequency Questionnaire (FFQ) and the Finnish Diabetes Risk Score (FINDRISC) within a digital platform to capture both dietary patterns and individual risk factors. A cross-sectional design was applied to 110 undergraduates, chosen to reflect young adults most vulnerable to lifestyle-related DM risks. Data were collected entirely online to ensure feasibility and low-cost scalability in campus and public health programs. Multiple linear regression revealed that both individual factors (age, gender, BMI, physical activity, family history) and dietary patterns were significant predictors of DM risk (β = 0.312; β = 0.389; p < 0.001), explaining 37.4% of the variance. Compared to prior studies that relied solely on clinical or genetic markers, this integration highlights the added predictive value of dietary data in digital risk screening. With 70.9% of respondents at moderate and 25.5% at high risk, the findings underscore the urgent need for early intervention among Indonesian students. The proposed model offers practical applications through university health centers, mobile apps for student lifestyle monitoring, and peer-based preventive education. Future work should extend to biomarker validation and adaptive algorithms to enhance predictive accuracy and applicability across diverse populations.]]>
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