Skin Manifestations and Their Association with Prediabetes in the Pediatric Population
Keywords:
Pre-diabetes, non-invasive diagnosis, paediatric, image analysis, machine learning, metabolic markersAbstract
The rising prevalence of prediabetes in children and adolescents is a serious concern that requires early intervention to prevent progression to diabetes. Lifestyle and diet are the main social factors impacting health. Early identification of at-riskyouth through noticeable skin and health symptoms can support reversal through lifestyle changes. Effective screening methods should target children and youth at risk using non-invasive approaches. This paper proposes non-invasive methods for early prediabetes detection in children and adolescents. Machine learning and deep learning models can analyze physiological data for early diagnosis.
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