HIGHLIGHTS
SUMMARY
In recent years, machine_learning has been rapidly developed and widely used in clinical diagnosis. Clinical diagnosis based on machine_learning regards the disease diagnosis process as a prediction problem characterized by the clinical manifestations of the disease. According to the clinical manifestations of the disease, the feature space of the sample is established, and the existing cases and diagnostic results are used as the training set of the machine_learning model, so that the new cases can be predicted. The problem to be solved in a clinical diagnosis based on machine_learning is sample imbalance. When trained . . .
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