## 3 Results
Figure 3: Dimensionality reduction visualizations employing PCA, UMAP, and t-SNE have been generated in both two-dimensional (2D) and three-dimensional (3D) formats. In these visualizations,  
Figure 2: Correlation heatmap that encompasses the nine variables selected for the scope of this investigation. Each cell within the heatmap is color-coded to represent the corresponding correlation value, which spans from \(-1\) for variables exhibiting a negative correlation (indicated by white color) to \(1\) for variables demonstrating a positive correlation (signified by purple color). As anticipated, all elements situated on the principal diagonal of the plot are rendered in deep purple, denoting the correlation of a variable with itself. Notably, the target variable of the dataset (profile) manifests a significant positive correlation with the constructed age range and, to a lesser extent, with the TNF, IL-6, and IL-1\(\beta\) variables. each data point, representing an individual sample, is distinguished by colors corresponding to its profile value (\"health', 'other', 'neuro'), as denoted in the legends of the figures. Across all three dimensionality reduction techniques, it is observable that the data points cluster together, with UMAP's 2D visualization offering the most defined grouping among all configurations examined. Specifically, in the UMAP 2D plot, four distinct clusters are identifiable; one of these clusters comprises samples characterized by both 'neuro' and 'other' profiles, while the remaining three clusters encompass samples with 'neuro' and/or 'other' profiles. The key features were consistently emphasized across all dimensionality reduction techniques, as determined by the established feature selection scheme. As previously noted, a comprehensive evaluation was conducted using the top four features across various dimensionality reduction algorithms, including both PCA and UMAP. The results from these analyses indicated no significant differences when compared to those obtained using the full set of features. Consequently, the conclusions regarding cluster separation remain unchanged, irrespective of the number of features utilized. Figure 4: A line/dot plot has been constructed to display the outcomes derived via the Borda feature importance consensus methodology. On this plot, the y-axis enumerates the names of the features present in the dataset, while the x-axis quantifies the importance values ascertained through the Borda rank-based count. The examination of this plot reveals the concordance between the outcomes of the consensus feature importance scheme and the patterns observed in the preceding correlation heatmap; specifically, the age range emerges as the predominant factor in differentiating among the three categories of the output variable (profile), mirroring its position as the most significantly positively correlated feature in the heatmap. A similar relationship is observed for the features IL-6, IL-1\(\beta\), and TNF, underscoring their relevance in accordance with both the consensus feature importance scheme and the correlation heatmap. The decision tree classifier that was also applied has been configured to balance complexity and interpretability. The tree facilitated the clear visualization of the feature space's segmentation, capturing essential data patterns and avoiding spurious correlations as can be seen in Figure 6. This approach allowed for transparent and interpretable model results, contributing to a comprehensive understanding of the data. The integration of detailed cellular observations with advanced data processing and machine learning techniques provided robust insights into the dataset. The findings underscore the potential of combining biological and computational methodologies to enhance the understanding and detection of [PERSON]'s disease, paving the way for the development of effective early diagnosis and prevention strategies.