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Overweight and obesity remain a major global public health concern andidentifying the individualized patterns that increase the risk of future weightgains has a crucial role in preventing obesity and numerous sub-sequentdiseases associated with obesity. In this work, we use a rule discovery methodto study this problem, by presenting an approach that offers genuineinterpretability and concurrently optimizes the accuracy(being correct often)and support (applying to many samples) of the identified patterns.Specifically, we extend an established subgroup-discovery method to generatethe desired rules of type X -> Y and show how top features can be extractedfrom the X side, functioning as the best predictors of Y. In our obesityproblem, X refers to the extracted features from very large and multi-site EHRdata, and Y indicates significant weight gains. Using our method, we alsoextensively compare the differences and inequities in patterns across 22 stratadetermined by the individual's gender, age, race, insurance type, neighborhoodtype, and income level. Through extensive series of experiments, we show newand complementary findings regarding the predictors of future dangerous weightgains.

Identifying the Leading Factors of Significant Weight Gains Using a New Rule Discovery Method