## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **PV** & \(\beta\) & **S.E.** & **Wald \(\chi^{2}\)** & _p-Value_ & **OR** & **95\% CI of OR** & **AUC (95\% CI)** \\ \hline \multirow{4}{*}{Model 1} & SLS adjusted & \(-\)0.582 & 0.289 & 4.068 & 0.044 * & 0.559 & 0.317 to 0.984 & \\  & PSI \(<\) 0.75 & \(-\)1.708 & 0.747 & 5.227 & 0.022 * & 0.181 & 0.042 to 0.784 & 0.832 \\  & SRS error & 0.348 & 0.172 & 4.098 & 0.043 * & 1.416 & 1.011 to 1.983 & (0.725–0.938) \\  & Constant & 1.71 & 1.199 & 0.02 & 0.887 & 1.186 & & \\ \hline \multirow{4}{*}{Model 2} & SLS adjusted & \(-\)0.584 & 0.290 & 4.048 & 0.044 * & 0.557 & 0.315 to 0.985 & \\  & PSI \(<\) 0.75 & \(-\)1.59 & 0.765 & 4.321 & 0.038 * & 0.204 & 0.045 to 0.913 & 0.840 \\ \cline{1-1}  & SRS error & 0.322 & 0.176 & 3.347 & 0.067 & 1.380 & 0.977 to 1.948 & (0.735–0.946) \\ \cline{1-1}  & Age adjusted & 0.061 & 0.079 & 0.603 & 0.438 & 1.063 & 0.911 to 1.242 & \\ \cline{1-1}  & Constant & \(-\)0.327 & 1.366 & 0.057 & 0.811 & 0.721 & \\ \hline \hline \end{tabular} PV, Predictor Variable; S.E., Standard Error; OR, Odds Ratio or Exp (\(\beta\)); AUC, Area Under the Curve; CI, Confidence Interval; SLS, Single Limb Support; PSI, Postural Stability Index; SRS, Sentence Recognition Score. * significance level _p-value_ \(<\) 0.05.  
\end{table} 
Table 6: Logistic regression characteristics, models 1 and 2. The results of pure tone audiometry revealed a similar hearing level in the two groups, with 50% of participants in each group having a hearing impairment. However, the number of mistakes in the sentence recognition test displayed a lower level of ability in LC. One observation is that the impact of cognitive ability on this score is greater than the impact of hearing ability. Research has shown that sentence comprehension is lower in people with dementia [27, 28]. The existence of a significant difference between the two groups was present in several variables, thus making them candidates for predictor variables. However, not all of these variables possessed high predictive ability (represented by their AUC) and therefore were not considered for further analysis. Of the variables that represented gait function, only SLS exhibited high predictive ability. In the case of postural stability, both PSI and APSI had high AUC values, and in the case of auditory function, it was the SLS and SLS error variables. One variable representing each function (gait, balance, and auditory function) was chosen for use in the logistic regression model. Based on the AUC values and their discriminatory ability, SLS, PSI < 0.75, and SRS error were used to design the logistic regression models. Model 1 was designed using only the three aforementioned variables, and model 2 was designed to adjust for the effect of age as a confounding factor. Regarding the model characteristics, the \(\beta\) coefficients show the influence of a feature on the probability of having lower cognition. In the case of SLS, the negative coefficient signifies that an increase in the single limb support portion decreases the possibility of lower cognition. By observing the odds ratios (OR), it can be concluded that having a PSI lower than 0.75 lowers the possibility of having cognitive impairment by approximately 80% (OR = 0.181, model 1, and OR = 0.204, model 2). Similarly, an increase in the duration of the SLS portion of the gait cycle lowered the probability of cognitive impairment by 44% (OR = 0.559, model 1, and OR = 0.557, model 2). In contrast, an increase in the number of mistakes during the sentence recognition test resulted in a 40% increase (OR = 1.416, model 1, and OR = 1.38, model 2) in the likelihood of having cognitive impairment. The age and constant can be removed from the models because of their non-significant contribution, leaving model 1 as the best model obtained from this dataset. Based on the AUC values, it can be concluded that the models have excellent predictive ability.