## 3 Results and Discussion  
Tables 2-4 present the results of the PR and CD-based isolated models. Each table lists the five ML algorithms used, with the best-performing model highlighted in bold. For the CD-based models (Table 2), ANN achieved the best performance. However, all models--except KNN--produced similar results (see Table 5 for hyperparameter details). Tables 3 and 4 display the models trained using features extracted from patient documents. Among the two PR analysis methods, the TF-IDF representation yielded the best result, with an AUC of 60.8%, a 9% lower than that of the CD-based models. Figures 4 and 5 exhibit the dispersion of the results through boxplot graphics. SVM were chosen, as they demonstrated better performance compared to the other models. The ANN model was excluded since it was already incorporated into the architecture shown in Figure 3. Additionally, two models from Table 3 were selected, as the TF-IDF method outperformed the embeddings approach. Again, LR and SVM were chosen as the most effective algorithms. It is important to note that the AUC metric is not available for the voting classifier, as it was not possible to estimate a probability function based on the votes. Figure 7 visualizes the ROC curve for the best performance of the models in the cross-validation technique. When comparing the performance of the four proposed schemes, the CD-based models achieved the highest AUC values, indicating the best results. While combining information from different sources also enabled patient classification, the overall model performance remained unchanged. However, data fusion remains a valuable approach for supporting diagnostic decision-making, particularly in cases where certain patient information is unavailable. For example, some patients may avoid disclosing their HIV status or other critical details. In such situations, patient interviews and PR data can be leveraged to enhance diagnostic accuracy and compensate for missing information. Despite achieving a sensitivity of 73.6% when PR was represented using embeddings and SVM model, and a value of 80.3% for TF-IDF representation and RF model, the AUC values (56.0% and 53.2%) remained lower than that of the CD-based models (see Table 2). Comparable studies have reported an AUC of 71% and a sensitivity of 77% using a similar set of clinical variables, where an ANN model achieved the best performance [11]. Additionally, in a resource-limited setting, sensitivity values as high as 97% have been reported [12]. However, specificity values in these studies were relatively low, recorded at 23% and 71%, respectively. It is important to note that, despite these similarities, differences in the variables used and preprocessing techniques influence the final results. Regarding the data fusion models, Type A outperformed Type B, achieving an AUC of 69.3% compared to 67.2%. However, the sensitivity of the Type B model reached 71.3%, making it comparable to widely used smear microscopy tests, which typically have a sensitivity ranging from 40% to 60%, the first feasible diagnostic option in the Colombian protocol. Ideally, PR should have provided additional information, yet the results remained similar between CD-based models (AUC = 69.9%) and the best data fusion model (AUC = 69.3%), with a drop of less than one percentage point. Notably, the best PR-based approach achieved the highest sensitivity (73.6%) (see Table 4), which is particularly valuable when not all data sources are available. In such cases, health professionals can assess multiple diagnostic models and make more informed decisions rather than relying solely on CD or PR in isolation. A distinctive aspect of this research is its emphasis on leveraging available data to train models in resource-constrained healthcare settings. Many developing countries face significant challenges, including inconsistent health data collection, restricted data availability, and shortages of healthcare personnel, all of which impact the development of reliable models.