## 4 Discussion
As shown in Figure 2, all algorithms effectively classify non-FH patients (the best true negative ratio of 0.960 was obtained for SVM), though model performance drops slightly for FH patient classification which is reflected by recall (the best recall of 0.709 was obtained for NN). This issue was addressed through model ensembling, employing two ensemble methods: voting and stacking. Results in Tables 2 and 4 indicate that ensembling enhanced accuracy, although Figures 3 and 4 show only modest improvements in FH classification among patients with confirmed DNA mutations. Both ensemble techniques were effective. However, the best voting results were achieved with three models (reflected by the highest F1-Score: 0.750), while stacking showed strong performance with an ensemble of two models (reflected by the highest F1-Score: 0.745). To compare models to the accuracy of the DLCN score-based classification of the FH, the positive class accuracy was checked. The results are presented in Table 5. It can be noticed that models fail to recognize the cases of FH in the low probability group of patients with DLCN scores between 0 and 2 but the stacked Cat-SVM model achieved nearly 70% accuracy in classifying positive FH patients in all other groups (possible, probable, and definite). The presented results show that the ensemble model can give a reliable assessment of FH if the patient has a DLCN score between 3 and 5 without DNA confirmation. The accuracy of the DLCN score was lower than the proposed machine-learning models, indicating that an AI-based approach may offer a more precise and reliable method for FH classification. These results suggest that models like CatBoost can enhance diagnostic accuracy and reduce the variability inherent in the DLCN score. Additionally, this study demonstrates that model ensembles provide an advantage in the classification of FH patients, further supporting the potential of AI to improve diagnostic outcomes. Moreover, the statistical significance of conducted studies was checked with [PERSON]'s test. Due to the ordinal nature of the DLCN score, the direct comparison with machine-learning models is limited. Therefore, to provide a statistical comparison, the DLCN score was transformed into binary outcomes using two methods: threshold and logistic regression. The results presented in Table 6 indicate statistically significant differences (\(p<0.05\)) between DLCN-derived outcomes and the models. This shows that proposed machine-learning models may provide a meaningful improvement over traditional clinical scoring methods in classifying FH. The tested models demonstrated high accuracy and precision, indicating that they effectively identified FH patients while minimizing false positives and false negatives. The F1-Score, which balances precision and recall, further confirmed the robustness of the model in handling this classification task. This suggests that machine-learning models can help to conclude genetic test recommendations towards FH. The selection of the methods proposed in this study was justified by the nature of the dataset. Conducted tests showed that incorporating more complex methods like the one based on neural networks could provide good recall (NN obtained highest recall = 0.709) but lower precision (0.727 for TabNet and 0.611 for NN). This can be explained by the fact that neural networks generally perform better on unstructured data, such as images. Alsoin this study, a strict method of handling the missing data was adopted. Considering the size of the utilized dataset, the approach based on model ensembles seems to be less prone to overfitting in comparison. It should be mentioned, though, that more advanced methods based on deep neural networks could be used to incorporate other data types, such as results of imaging diagnostics to further enhance the model's predictive capabilities and provide a more comprehensive assessment.