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.

Since 92% of patients are all from one class, you can build a classifier that just predicts the majority class for all samples and get 92% accuracy. This is of course not a terribly interesting classifier, as it completely ignores everything about individual patients, and makes a prediction based on the population as a whole. Classifiers are generally expected to make a sample-level prediction based on some set of input features, but a naive majority-class classifier can achieve 92% accuracy  in this setting without knowing anything at all about individual patients. In some cases where accuracy is the right measure to optimize, the majority-class classifier may be a useful baseline comparator, since it represents an accuracy benchmark that can be achieved with the simplest possible model.
That said, I disagree that any useful classifier must have greater accuracy than the majority-class prevalence. Accuracy is often not the best measure of classifier performance, particularly in cases where the "cost" of errors is not equal between false positives and false negatives. Accuracy treats all misclassification errors as the same, but there can be very real differences in the consequences of making false positive versus false negative errors.
Imagine we are building a classifier for a medical screening test for a serious, fast-progressing, but relatively rare disease. In such a case we want a test with very high sensitivity (we can't afford to miss patients who truly have the disease, since they will die if the disease continues untreated), but are willing to sacrifice specificity to achieve it (we're willing to falsely diagnose some people and send them for a follow-up test). In such a case, we want very high accuracy in the small disease-positive population (resulting in few false negatives), but may allow lower accuracy in the large disease-negative population (resulting in somewhat more false positives). The net accuracy may be lower than the majority-class prevalence, but the overall "cost" of misclassifications is actually lower than other classifiers with higher accuracy, since not all errors are equivalent.
