## 3 Results and Discussion  
Three algorithms were used in this study to model the complex relationships in the dataset. RF was selected as the better-performing model of the three models and has practical suitability for deployment, considering its balance between precision and recall. While MLP displayed limitations in recall and F1 scores for specific diseases, suggesting possible limitations in handling imbalanced data within the dataset, XGBoost showed promising results but required intensive tuning. Across all models, the Random Forest algorithm emerged as the best-performing model and achieved the highest diagnostic performance across most diseases with an F1 score of 88% for malaria, 60% for enteric fever, 51% for HIV and AIDS, 72% for urinary tract infection, 72% for respiratory tract infection, and 60% for tuberculosis, followed by XGBoost (87%, 60%, 48%, 70%, 72%, and 65%) and the MLP (85%, 51%, 46%, 70%, 69%, 64%) model, as presented in Table 5 and Figure 6.  
All three models demonstrated strong performance for malaria, with Random Forest (F1 score = 0.88) achieving the highest performance, followed closely by XGBoost (0.87) and MLP (0.85). This trend suggests that malaria symptoms are well-represented in the dataset, making them easier to classify. Additionally, RF's high recall (0.91) indicates its effectiveness in detecting malaria cases, likely due to its ability to handle structured tabular data efficiently. The classification of enteric fever was relatively weak, especially in recall, with MLP (0.42), XGBoost (0.56), and RF (0.53). This suggests a class imbalance issue, where enteric fever cases are underrepresented in the dataset. However, RF achieved the highest precision (0.69), likely due to its ensemble learning strategy, which improves robustness when dealing with imbalanced data. HIV/AIDS classification was notably challenging, with MLP (F1 score = 0.46), XGBoost (0.48), and RF (0.51). The low recall scores (ranging from 0.34 to 0.40) indicate that many true HIV/AIDS cases were misclassified. This can be attributed to the non-specific nature of early HIV symptoms, which overlap with other febrile illnesses. However, RF achieved the highest precision (0.75), suggesting it was more conservative in its classifications. UTI classification showed moderate performance, with MLP (F1 score = 0.70), XGBoost (0.70), and RF (0.72). RF outperformed the others in precision (0.80), meaning it had fewer false positives, while MLP had better recall (0.65), suggesting it detected more actual UTI cases. This trade-off highlights the importance of \begin{table}
\begin{tabular}{l l l l l l l l} \hline  & & **MAL** & **ENFVR** & **HVAD** & **UTI** & **RTI** & **TB** \\ \hline \multirow{3}{*}{MLP} & Precision & 0.84 & 0.65 & 0.75 & 0.77 & 0.75 & 0.59 \\  & Recall & 0.87 & 0.42 & 0.34 & 0.65 & 0.63 & 0.59 \\  & F1 score & 0.85 & 0.51 & 0.46 & 0.70 & 0.69 & 0.64 \\  & Precision & 0.84 & 0.64 & 0.62 & 0.77 & 0.77 & 0.72 \\ XGBoost & Recall & 0.90 & 0.56 & 0.40 & 0.63 & 0.68 & 0.60 \\  & F1 score & 0.87 & 0.60 & 0.48 & 0.70 & 0.72 & 0.65 \\  & Precision & 0.85 & 0.69 & 0.75 & 0.80 & 0.77 & 0.77 \\ RF & Recall & 0.91 & 0.53 & 0.39 & 0.65 & 0.68 & 0.49 \\  & F1 score & 0.88 & 0.60 & 0.51 & 0.72 & 0.72 & 0.60 \\ \hline \end{tabular}
\end{table} 
Table 5: ML diagnostic model performance. Figure 6: Performance evaluation of the ML models. balancing precision and recall based on the diagnostic goal (e.g., reducing false negatives vs. false positives). RTI classification was relatively stable, with XGBoost and RF achieving identical F1 scores (0.72), while MLP had a slightly lower F1 score (0.69). The recall values (0.63 to 0.68) indicate that all models were moderately effective at detecting RTI cases, possibly due to their distinct symptom profiles. For tuberculosis, the models showed significant variation. MLP achieved the lowest F1 score (0.64), while XGBoost and RF performed slightly better (0.65 and 0.60, respectively).