## 2 Methods
### Classification Algorithm  
Due to the nature of the dataset, which combined numerical and boolean data, it was decided to evaluate the machine-learning algorithms for classification including Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Random Forest (RF), and Support Vector Machine (SVM). Moreover, the feed-forward neural network and TabNet architectures were also utilized. XGBoost and CatBoost are gradient-boosting algorithms that are particularly effective for handling categorical variables in machine-learning tasks. They both belong to the family of gradient-boosting algorithms, which sequentially combine multiple weak learners to create a strong predictive model. CatBoost uses an efficient method to convert categorical variables into numerical values during training, which reduces the pre-processing burden and potential information loss. The CatBoost is being considered more robust to overfitting due to implemented order-boosting. The XGBoost incorporates advanced regularization techniques, such as L1/L2 regularization. However, it needs to be addressed during hyperparameter tuning. In general, the XGBoost is considered to be more prone to overfitting than CatBoost. The other two algorithms are also widely used machine-learning methods for classification tasks. Support Vector Machine is particularly useful for high-dimensional data, as it can handle non-linearly separable data through the use of kernel functions. This feature can be useful in terms of FH classification, where the identification of complex patterns is desired. Random Forest, on the other hand, is an ensemble method that builds multiple decision trees. It is considered to be robust and capable of handling imbalanced datasets, which is often the case in medical diagnosis studies. All these algorithms have advantages and disadvantages. Nevertheless, they seem to be a suitable choice for the study aimed at classifying FH probability. For this study, the feed-forward neural network was proposed. The network consisted of three hidden layers with 128, 64, and 32 neurons, respectively, each using the ReLU activation function. A dropout regularization of 0.3 was applied after each hidden layer. The network was designed for binary classification; therefore, the sigmoid activation function was used as well as the binary cross-entropy loss function. TabNet [25] was the last model evaluated in this study. The TabNet model is designed to handle tabular data. It has an encoder-decoder architecture with a decision-step approach to dynamically select and process features. The models utilized in this study were selected due to the nature of the dataset. The performance of the selected models relies on their hyperparameters. Therefore, optimizing them is crucial for improving model accuracy and generalization. For this study, it was decided to use a grid search to identify the optimal hyperparameters for the models. This systematic approach involves defining a grid of hyperparameter values andevaluating model performance across all possible combinations. Grid search optimization turned every positive result in other medical-related research, like the one focusing on predicting HIV/AIDS test results, improving prediction accuracy and robustness [26]. The mathematical simplicity of this approach is one of its advantages. The downside is that it requires certain knowledge of the models and their hyperparameters to create the list that helps to narrow the search. Model performance was assessed using accuracy as the scoring method. The hyperparameter combination providing the highest score was selected as the optimal configuration for each model. For different models, a different list of hyperparameters was defined. For the XGBoost, the optimized parameters were:  
1. Sampling predictors
2. Sampling observations
3. The learning rate
4. The maximum depth of a tree
5. The number of trees
6. Minimum loss reduction required to make a further partition on a leaf node of the tree
7. Regularization parameter  
For Random Forest:  
1.