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- W2267305654 abstract "The complex network of diet, lifestyle and socio-demographic exposures are hampering the identification of groups that are more likely than others to become obese. Traditional regression models are often limited in exploring the mutual importance of exposures: we cannot make very good inferences about what variables are actually important and why. Machine learning techniques may provide a solution to investigate the network between exposures and eventually develop decision rules for obesity risk estimation in clinical work. The population-based cross-sectional study …" @default.
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- W2267305654 date "2013-10-01" @default.
- W2267305654 modified "2023-10-14" @default.
- W2267305654 title "Random forest analysis in identifying the importance of obesity risk factors" @default.
- W2267305654 doi "https://doi.org/10.1093/eurpub/ckt124.042" @default.
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