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In contemporary society, the escalating pressures of life and work havepropelled psychological disorders to the forefront of modern health concerns,an issue that has been further accentuated by the COVID-19 pandemic. Theprevalence of depression among adolescents is steadily increasing, andtraditional diagnostic methods, which rely on scales or interviews, proveparticularly inadequate for detecting depression in young people. Addressingthese challenges, numerous AI-based methods for assisting in the diagnosis ofmental health issues have emerged. However, most of these methods center aroundfundamental issues with scales or use multimodal approaches like facialexpression recognition. Diagnosis of depression risk based on everyday habitsand behaviors has been limited to small-scale qualitative studies. Our researchleverages adolescent census data to predict depression risk, focusing onchildren's experiences with depression and their daily life situations. Weintroduced a method for managing severely imbalanced high-dimensional data andan adaptive predictive approach tailored to data structure characteristics.Furthermore, we proposed a cloud-based architecture for automatic onlinelearning and data updates. This study utilized publicly available NSCH youthcensus data from 2020 to 2022, encompassing nearly 150,000 data entries. Weconducted basic data analyses and predictive experiments, demonstratingsignificant performance improvements over standard machine learning and deeplearning algorithms. This affirmed our data processing method's broadapplicability in handling imbalanced medical data. Diverging from typicalpredictive method research, our study presents a comprehensive architecturalsolution, considering a wider array of user needs.

Exploration of Adolescent Depression Risk Prediction Based on Census Surveys and General Life Issues