## 3 Results
### Inference of the CNN Model on the EGD Data Set for Glioma Detection  
The model was evaluated in the EGD data set, which contains clinical history data such as age and sex, to determine correlations with the model prediction. Figure 8 summarises the model predictions in the Erasmus database, composed mainly of patients with glioma. Consequently, the bar graph represents only the _TP_ and _FP_ counts, while _TN_ and _FN_ values were absent due to the database inherent design. The results showed that the model was generalizable across both age and sex groups, achieving an F1-score of 0.88 (Table 6). However, in older patients, model predictions were more accurate, with an average accuracy of 57.29 years (\(\pm\)14.68) compared to 54.55 years (\(\pm\)14.22) of incorrect predictions (Table 7). Furthermore, a sex-based comparison revealed a slightly higher correct prediction rate for men (79.39%) than women (77.70%) (Table 8). \begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Value** \\ \hline F1-Score & 0.88 \\ Precision & 0.78 \\ False Negative Rate & 0.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Model performance metrics. Figure 8: Bar chart illustrating the counts of false negatives (_FNs_) and true positives (_TPs_) in the predictions of the model on the EGD data set. Although both groups exhibited comparable performance, the slight variations based on demographic factors suggested the possibility of subtle influences. As illustrated in Figure 9, the model demonstrated suboptimal predictive accuracy for patients aged 80-81 years, with correct prediction rates falling below 50%. This led to an investigation into the potential bias in model predictions. Statistical Analysis for Assessment of Demographic Bias  
To analyse any significant variation in model performance, the patient cohort was stratified into three age groups: young (19-26 years), adults (27-59 years), and older adults (60-100 years) for both sexes, as proposed in [41]. As illustrated in Figure 10, this age segmentation revealed a trend among age groups of both men and women, with the highest percentage of errors observed in adults, followed by older adults and then young patients, and slightly more pronounced in women. This led to the application of statistical tests to corroborate whether demographic factors, such as age and sex, exerted a significant influence on model performance and may have contributed to potential discrepancies in the predictions. To assess the normal age distribution in the _FN_ and _TP_ groups (Figure 8), the Shapiro-Wilk test was applied under the following hypotheses:  
**H0. ** The data follows a normal distribution. **H1. ** The data do not follows a normal distribution. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Prediction** & **Age (M \(\pm\) SD)** & **Range** & **Preds** & **M** & **F** \\ \hline Incorrect & 54.55 \(\pm\) 14.22 & 21–82 & 155 & 94 & 61 \\ Correct & 57.29 \(\pm\) 14.68 & 19–86 & 570 & 362 & 208 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistical summary of predictions by age and sex. Figure 9: Percentage of correct and incorrect model predictions for each age in the EGD data set. The age 83 is omitted due to the absence of samples in the data set. As outlined in Table 9, the results of the Shapiro-Wilk test showed that the obtained \(p\)-value for the \(F\!N\) group followed a normal distribution. Conversely, the \(TP\) group exhibited a \(p\)-value lower than \(\alpha\), indicating a deviation from normality (Figure 11). Regarding the confidence intervals, the slight overlap between the intervals 56.08 and 56.79 suggested that the difference between the two groups was not significant. To evaluate differences in age distributions between prediction outcome groups within each cohort, the Mann-Whitney U test was applied under the following hypotheses:  
**H0. ** The distribution of ages does not differ between groups. **H1. ** The distribution of ages differs between groups.