aceIdentification of obesity risk factors in 3-12-year-old children and adolescents with prior respiratory tract infections via interpretable machine and deep learning models
Childhood obesity and respiratory tract infections (RTIs) are 2 major global public health issues that frequently co-occur and are closely interrelated. Early detection of children with prior RTIs who are at high obesity risk is crucial for targeted interventions. This study integrates interpretable machine learning (ML) models and a deep learning network to develop an obesity risk prediction model in a large pediatric cohort. Cross-sectional data from 6509 children and adolescents aged 3-12 years with prior RTI…
LightGBM-based model predicted obesity versus normal weight in children aged 3-12 with prior RTIs with high accuracy and AUC, enabling early screening for targeted intervention.
Findings are based on cross-sectional data limited to children aged 3-12 years with prior RTIs in Beijing and Tangshan, constraining generalizability and causal inference.
Evidence
- Peer-reviewedJAMIA Open2026-08-06
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Truvace Impact Record TRV-2026-0690, v1: “Identification of obesity risk factors in 3-12-year-old children and adolescents with prior respiratory tract infections via interpretable machine and deep learning models.” Truvace, 2026-08-08. /record/TRV-2026-0690 (accessed at citation time). sha256 61fb430ddf44f9c8…
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