What is the basis behind "a good classifier must be significant better than $92\%"?$ 
The problem is to determine whether a patient referred to the clinic is hypothyroid. Therefore three classes are built: normal (not hypothyroid), hyperfunction and subnormal functioning. Because 92 percent of the patients are not hyperthyroid a good classifier must be significant better than 92%.

This is for some data I found for Thyroid Disease.


a good classifier must be significant better than 92%.

There are two issues here

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*Where does this statement come from?
It is not so clear what is meant with the 92%, but it seems to relate to accuracy. $$ \text{accuracy} = \frac{\text{good estimates}}{\text{total estimates}} $$ this 92% accuracy can already by achieved with a naive classifier that assigns every patient to the negative category. For the 92% of the patients that are negative it will be right and for the 8% of the patients that are positive it will be wrong.
But also

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*Is this statement right?
I disagree. The classifier does not need to be better than 92%.
Imagine the following situation:
Your test could be 100% sensitive and classify 8% of the people as having hyperthyroid disease while they also have the disease, but it also accidentally classifies 9% of the people as having hyperthyroid disease while they actually do not have hyperthyroid disease. Then you make a mistake in 9% of the cases and the accuracy is 91%, which is less than 92%.
Did you do bad?
