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Obesity is a common, serious, and costly chronic disease in the United States, a risk factor for several major cancers, linked to higher rates of illness and death. It is thus a critical issue that needs attention from health care professionals and the public alike. We use a novel approach to target nonstandard variations to better understand the variables associated with weight loss. We introduce a new methodology using the Choquet Integral with fuzzy measure, an approach that accounts for interactions between measured features. The Choquet Integral has limited sourced applications to the biomedical field despite widespread use in theoretical mathematics and economics. Our technique applies it to health data to show a robust method to target and optimize weight loss parameters. We identify data versus noise, optimally choose a reduced version of the powerset for computability purposes, and identify the sub-additive cooperative learning bound of the Choquet Integral. We show that the proposed technique targets heretofore unknown variations in predictive weight loss studies with broad potential applications.