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Wang et al [22] formulated chronic fatigue syndrome differentiation as a multilabel learning task. Combining random forest, conformal prediction framework, and problem transformation methods, they established a reliable diagnostic tool with large-scale confidence levels from 80%-100%.
In accordance with the universal approximation theorem, a deep neural network with a given number of hidden layers should be able to approximate any function that exists between input and output [23].
JMIR Med Inform 2020;8(6):e17821
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