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Background: Overweight and obesity are an increasing phenomenon worldwide.Predicting future overweight or obesity early in the childhood reliably couldenable a successful intervention by experts. While a lot of research has beendone using explanatory modeling methods, capability of machine learning, andpredictive modeling, in particular, remain mainly unexplored. In predictivemodeling models are validated with previously unseen examples, giving a moreaccurate estimate of their performance and generalization ability in real-lifescenarios.  Objective: To find and review existing overweight or obesity research fromthe perspective of employing childhood data and predictive modeling methods.  Methods: The initial phase included bibliographic searches using relevantsearch terms in PubMed, IEEE database and Google Scholar. The second phaseconsisted of iteratively searching references of potential studies and recentresearch that cite the potential studies.  Results: Eight research articles and three review articles were identified asrelevant for this review.  Conclusions: Prediction models with high performance either have a relativelyshort time period to predict or/and are based on late childhood data. Logisticregression is currently the most often used method in forming the predictionmodels. In addition to child's own weight and height information, maternalweight status or body mass index was often used as predictors in the models.

Predicting overweight and obesity in later life from childhood data: A review of predictive modeling approaches