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All the five machine learning methods had higher sensitivities but lower specificities than human doctors.
Compared with human doctors from our institute, all the five machine learning methods had higher sensitivity but lower specificity than human doctors.
As expected, laboratories using primarily rapid antigen tests had lower estimated sensitivities, and laboratories that used PCR methods had higher sensitivity estimates.
The combination of methods had higher sensitivity (88.0% vs 67.4%) but reduced specificity (88.0% vs 69.5%).
With the equicorrelated variance structure, EE(Wald), SL and ML methods had higher power than the other four methods.
Giachino et al [ 31] also used NN to identify significant predictors, in Crohn's disease, but again saw that other analytical methods had higher predictive accuracy.
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Some computational examples show that the present methods have higher accuracy, and can meet the need of engineering design.
In Project WiwiKom, the test items on investment appraisal methods have higher cognitive requirements for decision-making in finance.
Regardless of the alert rate used, the modified methods have higher sensitivity.
Observation data showed that hypervolemic maintenance hemodialysis (MHD) patients identified using BIA methods have higher mortality risk.
Overall, both methods have higher recall than precision, showing that false positive errors are more common than false negative errors.
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