## 5 Results and Discussion
These results also re-establish the fact that importance scores alone cannot be used as a metric for feature selection, as evident from Figure 11 in which almost none of the features deemed most important in a full feature set were selected during optimization by all the optimization algorithms. However, the distribution of importance scores still holds an effective role to assess how well the selection process performed for each algorithm comparatively. Moving on to the second part of our results, we focused on the application of the leading optimization algorithm, HSMO, with various feature extractors. As the features still remain numerous even after being optimized, in order to gain this insight, SHAP was once again used to distinctly map the per instance ranked distribution of impact score from each top feature of each extractor as indicated in Figure 12 for Edge Filters, Gabor Filters, HOG, and LBP, respectively. The feature importance illustrations are presented in the form of heatmaps, with their placement logically controlled through SHAP in order to highlight any arising patterns. The bars on the right of each heatmap indicate the individual impact of the listed feature in the classification as a whole. The feature distribution becomes unique for each extractor; therefore, in order to assess the impact of optimization, each of the heatmaps is shown in pairs, where the right heatmap indicates the heatmap of all features and the left indicates the heatmap of optimized features. Figure 11: Top: without optimization; left full features; middle BSMO and right BPSO; bottom: with optimization; left full features; and middle HSMO and right HPSO features. Figure 12: Feature heatmap using HSMO optimizer: (**a**) canny edge detector, (**b**) Gabor filters, (**c**) LBP; and (**d**) HOG—full and optimized feature heatmaps in right and left, respectively. As can be seen in Figure 12a, the most distinctive features were extracted through Edge Detection. The optimization of these extracted features resulted in a selection of key features, which can be seen from the top few optimized features being almost as important as all the low ranked features. Gabor Filters fared the worst out of these, perhaps due to limited search space (Figure 12b), as their ability to gauge textures requires a much larger explorative search. Another possible reason is that the extractor is applied on the thermal gradient rather than the images themselves. The full feature set heatmap indicates the scattered impact across features with no specific descriptors arising. Nonetheless, the optimization of these features improves the situation by shedding features that might have served as noise to features that held valuable information and impact. In Figure 12c,d, the HOG and LBP extractors enable the model to capture the most significant features, leading to clear and decisive predictions. The structure of their full feature heatmaps reflects this prediction, particularly in the confidence function at the top of the heatmaps, which shows a transition from the instances classified as healthy to those classified as sick in a much more defined way. The transition is especially sharp in the HOG heatmap, where the most distinctive features were extracted, which allowed for an evident increase in accuracy. The optimization process further refined these features, selecting key ones that contribute significantly to the model's performance. This is evident as the top optimized features hold nearly as much importance as all the low-ranked features combined. As a result, the model's confidence transition becomes steeper as it crosses the boundary where the instance changes class, indicating a well-learned, generalizable pattern. This consistent and precise decision boundary is crucial in a CAD system, as it enhances the trust that medical professionals place in the system's outputs, ultimately improving the system's reliability in clinical settings.