## 3 Results
### Implantation Recommendations Based on Multiple Hearing Loss Parameters  
Table 2 lists the model coefficients and \(p\)-values for the logistic regression. The coefficients may be interpreted as follows: Three hearing loss parameters showed significant effects. First, a 1 dB increase in I\({}_{\text{f}}\) increased the odds for a CI recommendation by a factor of exp(2.417). Second, the loss of one synapse per IHC increased the odds by a factor of exp(1.754). Finally, a 1 dB increase in O\({}_{\text{s}}\) increased the odds by a factor of exp(0.175). The coefficients of the other hearing loss parameters were non-significant. Figure 12: Minimum LAP achieved with (y-axes) and without (x-axes) implantation. The dashed lines are the lines of equality. The flat component of IHCL is color-coded. The shown types of hearing losses are as follows: (**a**) IHCL only, (**b**) CGL and IHCL, (**c**) IHCL + CS, and (**d**) CGL + IHCL + CS. Table 3 reports the confusion table of logistic regression, for which the hearing loss parameters were used as predictors. The true class ('implant' versus 'no implant') refers to the recommendation based on the minimum LAP obtained by the simulation. The predicted class refers to the output of the logistic regression. The numbers observed in the confusion table reflect excellent classification performance, since the implantation recommendation of the vast majority of the subjects is correctly predicted by the logistic regression. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Model Coefficients**} & \(\mathbf{p\text{-Value}}\) \\ \hline \multirow{5}{*}{**Model**} & \multirow{2}{*}{Intercept} & 4.875 & \textless{}0.001 \\ \cline{3-4}  & & \(\text{I}_{\text{f}}\) (dB) & 2.417 & \textless{}0.001 \\ \cline{3-4}  & & \(\text{I}_{\text{s}}\) (dB) & \(-\)0.053 & 0.079 \\ \cline{3-4}  & & \(\text{O}_{\text{f}}\) (dB) & \(-\)0.098 & 0.161 \\ \cline{3-4}  & & \(\text{O}_{\text{s}}\) (dB) & 0.175 & 0.007 \\ \cline{3-4}  & & \(\text{N}_{\text{fib}}\) & \(-\)1.754 & \textless{}0.001 \\ \hline \end{tabular} The parameters were the flat and sloping components of the loss of inner hair cells (\(\text{I}_{\text{f}}\) and \(\text{I}_{\text{s}}\)), the flat and sloping components of the cochlear gain loss (\(\text{O}_{\text{f}}\) and \(\text{O}_{\text{s}}\)), and the number of active fibers per IHC (\(\text{N}_{\text{fib}}\)), reflecting cochlear synaptopathy.  
\end{table} 
Table 2: Logistic regression coefficients for predicting cochlear implantation recommendation from hearing loss parameters \(\Psi\). Figure 13: Minimum LAP achieved with (y-axes) and without (x-axes) implantation. The dashed lines are the lines of equality. The number of functional synapses per IHC is color-coded. The shown types of hearing losses are as follows: (**a**) CS only, (**b**) CGL + CS, (**c**) HCL + CS, and (**d**) CGL + HCL + CS. Table 4 lists test statistics for the logistic regression. All the listed numbers reflect exceptionally high performance, since the sensitivity, the specificity, the positive and negative predictive values, and the accuracy estimates all exceeded 98%, and the positive and negative likelihood ratios were large and small, respectively. Table 5 reports the confusion table of the support vector machine, for which the hearing loss parameters were used as predictors. The numbers observed in the confusion table reflect excellent classification performance, which was even slightly better than the performance observed for logistic regression. Table 6 lists test statistics for the support vector machine. The small improvement as compared to logistic regression may have been due to the support vector machine's capability to model interactions between hearing loss parameters, enabled by the use of a quadratic kernel.