## 4 Discussion  
This study aimed to find the most effective method for using machine-learning algorithms to classify the presence of FH. To achieve this objective, the dataset contained the parameters that are commonly assessed in all patients during the preliminary diagnostic process and differential diagnosis of hypercholesterolemia, focusing on excluding secondary causes of the condition. This data from physical examinations, ECG results, and biochemical parameters are typically collected and requested in primary care settings before referring patients to specialized clinics. The hyperparameter optimization was a key focus. Various algorithms can be used for this purpose; however, it was decided to use grid search due to its applicability across all selected models. Additionally, recent studies indicate that grid search performs comparably to Bayesian and genetic algorithms [27]. Although grid search is widely used for hyperparameter selection, it requires pre-defining the hyperparameter values, after which it performs an exhaustive search across all specified combinations. This requirement is a limitation, making it computationally expensive. Consequently, the range of considered hyperparameters was limited. However, one advantage of grid search is that it can be universally applied to the chosen models and ensures that all possible parameter combinations are evaluated. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model 1** & **Model 2** & _p-Value_ \\ \hline DLCN bin. pred. & CatBoost & 0.000 (\(<\)0.05) \\ DLCN bin. pred & Voting Cat-RF-SVM & 0.000 (\(<\)0.05) \\ DLCN bin. pred & Stacking Cat-SVM & 0.000 (\(<\)0.05) \\ \hline logistic reg. & CatBoost & 0.000 (\(<\)0.05) \\ logistic reg & Voting Cat-RF-SVM & 0.000 (\(<\)0.05) \\ logistic reg & Stacking Cat-SVM & 0.000 (\(<\)0.05) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The results of McNemar’s statistical comparison. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DLCN Score** & **CatBoost** & \begin{tabular}{c} **Voting** \\ **Cat-RF-SVM** \\ \end{tabular} & \
\begin{tabular}{c} **Stacking** \\ **Cat-SVM** \\ \end{tabular} &
\begin{tabular}{c} **DLCN Score** \\ **Accuracy** \\ \end{tabular} \\ \hline \textless{}0, 2\textgreater{} & 0.286 & 0.429 & 0.286 & - \\ \textless{}3, \textless{}5\textgreater{} & 0.686 & 0.686 & 0.714 & - \\ \textless{}6, \textless{}8\textgreater{} & 0.615 & 0.692 & 0.692 & 0.310 \\ 9 and above & 0.714 & 0.857 & 0.714 & 0.350 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The comparison of the positive class accuracy calculated within groups with the accuracy of the DLCN score. Further, in this study, the selected machine-learning models were evaluated on a well-balanced dataset initially consisting of 1198 records. However, certain records had missing values in key parameters. Given the size of the dataset, it was decided to exclude records with missing data instead of applying interpolation or imputation methods. The motivation behind this decision was that interpolation or imputation could introduce bias or inaccuracies, particularly in a clinical context where the relationship between variables is complex, and that removing incomplete records ensured that the models were trained on high-quality, reliable data. Furthermore, the size of the dataset after removal remained sufficient for effective model training and evaluation, pre-serving the balance between classes and ensuring robust performance metrics. The resulting dataset consisted of 813 records. The selected models were trained on 80% of this data and tested on the remaining part. Table 1 shows that among tested models, Categorical Boosting and Random Forest algorithms provided the best results reflected by F1-Scores of 0.709 and 0.718 successively. The first one demonstrated higher accuracy (0.804 vs. 0.798), while the second had better recall (0.677 vs. 0.629).