## 2 Materials and Methods
### Experimental Design  
As mentioned earlier, five ML schemes were proposed for classification, as illustrated in Figure 2. These schemes include two approaches that use PR and CD separately and two fusion schemes that combine both sources of information: Type A and Type B data fusions. The ML approaches were applied to the PR-based, CD-based, and Type A fusion models. In contrast, the Type B fusion models were constructed based on the performance of the PR-based and CD-based approaches. This allows for selecting the best-performing ML algorithms for the classification task. Figure 2: The four proposed ML classification schemes. The Type B data fusion model incorporates an aggregation layer with two different strategies: (a) majority voting, where the final classification is determined by a simple majority vote among the four classifiers. In case of a tie, priority is assigned in descending order from model 1 (highest priority) to model 4 (lowest priority); (b) stacking classifier, where an ML algorithm is used as the aggregation layer. LR was chosen for this task, where the inputs are the probability outputs or decision functions (depending on the type of ML model) from the four classifiers. Additionally, a different model was introduced alongside the previously mentioned approaches. This model consists of a neural network architecture, as illustrated in Figure 3. The design incorporates two separate networks, each dedicated to learning from PR and CD data sources independently during the training process. These networks are then concatenated, allowing a subsequent layer to handle information fusion. Finally, the output layer consists of a single neuron with a sigmoid activation function, which performs the binary classification task. Stratified \(k\)-fold cross-validation (SKCV) was used to select the best parameters for text representation (TF-IDF and embeddings) and to optimize the hyperparameters of the ML models, as illustrated in Figure 1. In SKCV, the dataset was split into multiple partitions for training and validation, ensuring that each set remained distinct. The term \"stratified\" indicates that the class distribution was preserved across all partitions. Given the database size, a three-fold approach was chosen to maintain a sufficient number of samples in the validation set for an accurate model evaluation. To ensure statistical reliability, SKCV was repeated ten times, and the mean and standard deviation of the performance metrics across all repetitions were used to assess and compare the models. The parameters explored for obtaining the TF-IDF measure included different combinations of \(n\)-_grams_ as well as the maximum and minimum values of \(DF(t)\) required for a term to be included in the vocabulary. Additionally, the number of components used for dimensionality reduction was examined alongside the computed TF-IDF values. Both TF-IDF calculation and dimensionality reduction were fitted using the training set before being applied to both the training and testing sets. This approach ensures that when predicting the diagnosis of a document, the TF-IDF method does not incorporate vocabulary from the test set, preserving the integrity of the model evaluation. By doing so, test documents accurately simulate the addition of new, unseen inputs. For the Word2 Vec models training, the COBW and skip-grams neural network models were implemented using the training set. Several parameters were explored, including the window size and the minimum frequency a word needed to appear to be included in the vocabulary. Additionally, during the pooling step, both maximum pooling and mean pooling operations were applied. For both feature extraction methods, each document was represented by a vector, followed by a normalization step, where values were transformed into a range between \(-1\) and \(1\) to ensure consistency across representations. Figure 3: ANN used for information fusion (ANN fusion).