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Random forest uses both bagging (bootstrap aggregation), a successful approach for combining unstable learners, and random variable selection for tree building

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Documenting these alongside genetic predisposition data creates a comprehensive clinical narrative that helps insurers understand medical necessity beyond standard BMI or A1C thresholds alone