As described above, the evidential interval section is constructed for OCD and NOCD, and the higher probability value of OCD and NOCD was set as the final output value.
The characteristics of the initial PSA variable in the OCD and NOCD groups are shown in Table 1.
The Gleason scores in the OCD and NOCD groups are shown in Table 2.
The clinical T stages in the OCD and NOCD groups are shown in Table 3.
The output nodes of all classifiers were composed of two so that OCD and NOCD could be calculated with probability.
Sensitivity was defined as the probability of correctly matching NOCD. Because NOCD has less data than OCD, it is difficult to match.
In addition, as the DS computes probability, if one classifier predicts NOCD at a high number and the two classifiers predict a low number for OCD, then the NOCD is finally predicted based on the belief value of the DS algorithm.
The output can be OCD or NOCD in pathological staging (pT).
The neuro-fuzzy model and our proposed method aim to predict whether a patient has oCd (pT2) or NOCD (pT3+).