Machine learning classifiers for childhood obesity trajectrories using infant fecal microbiota data
About this project
Knowing obesity risk earlier could help healthcare professionals manage children’s weight more effectively. A GLMM-based classifier was built to predict children obesity with an AUC of 0.83, specificity of 73% and sensitivity of 85%, based on 2500 infants data.
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