## 3 Results and Discussion
allowed us to analyze the spatial distribution of the groups (Figure 9). The observed clear separation into two distinct areas confirmed the correct segmentation of the sample and demonstrated the ability of the model to identify the internal structure of the data without using class labels. Similar visualization was performed for the independent test dataset (df1), which was not used in the model training phase. The first four observations corresponded to patients without cardiovascular disease (class 0), while the last four patients belonged to the group with an established diagnosis of CVD (class 1). However, this information was deliberately not provided to the model during the prediction phase, as the aim of the study was to test the ability of the clustering algorithm to independently identify this separation solely based on internal patterns in the original features. This dataset was processed using the same normalization methods and projection into PCA space, after which cluster labels were predicted using the pre-trained k-means model. The resulting visual pattern (Figure 10) showed a stable and homogeneous clustering like the training sample, indicating the high robustness of the model and its ability to generalize to new data. The \"Cluster Label\" color scale in the graph indicates the belonging of each point to one of the two clusters extracted by the k-means algorithm, where zero and one are the numbers of the corresponding clusters. Figure 10: Predictive clustering of an independent medical dataset based on a pre-trained k-means model. Figure 9: Two-dimensional visualization of clustering in clinical patient data. Despite the fact that the model did not use class labels, it was known that the test sample df1 contained a hidden (implicit) structure corresponding to the true distribution of patients according to the presence of CVD. A comparison of the predicted clusters with the known but hidden information showed complete correspondence (Figure 11)--all patients were correctly assigned to the corresponding groups. This allowed the external validation of the clustering results, confirming the high accuracy and significance of the extracted features. Therefore, the analysis showed that the developed clustering model based on the k-means algorithm was highly effective when working with clinical data that did not contain explicit class labels. The model successfully identified the latent structure in medical indicators and demonstrated the ability to detect latent patterns specific to different patient groups. The external validation performed on an independent dataset showed the full agreement of the clustering results with the actual distribution of patients by cardiovascular health status, which confirmed the high accuracy of the model. Taking this into account, the proposed approach can be recommended for use in primary screening and preliminary patient segmentation tasks, especially in cases when the data do not contain annotated classes or are at the stage of primary analysis. The model has a number of limitations. It depends on the quality and completeness of clinical data, which may affect its generalizability. The selected biomarkers represent only part of the complex aging process. The model was trained on a specific cohort; therefore, further testing on broader populations is needed.