This research focuses on leveraging machine learning techniques to analyze molecular data, such as genetic and biomarker information, to identify and understand factors contributing to childhood obesity. The aim is to develop predictive models and insights that can aid in early diagnosis and intervention for this critical health issue.
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This research focuses on leveraging machine learning techniques to analyze molecular data, such as genetic and biomarker information, to identify and understand factors contributing to childhood obesity. The aim is to develop predictive models and insights that can aid in early diagnosis and intervention for this critical health issue.
Subaskar-S/Machine_Learning_in_the_Identification_of_Obesity_in_Children_Using_Molecular_Data
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This research focuses on leveraging machine learning techniques to analyze molecular data, such as genetic and biomarker information, to identify and understand factors contributing to childhood obesity. The aim is to develop predictive models and insights that can aid in early diagnosis and intervention for this critical health issue.
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