## 4 Research and Project Aimed at Developing Modules of a Classification and Expert System
### Final Classification After Treatment Module
#### 4.3.3 Achieved Results  
The precision scores for the evaluated machine learning models, shown in Figure 7, highlight key differences in classification performance. Precision, which measures the proportion of true positive predictions among all positive predictions, is crucial for minimizing false positives, particularly in clinical applications. Here is a summary of the results:  
* **Top performers: Bagging, LightGBM, logistic regression** and **random forest** achieved the highest precision scores (0.93 to 0.91), demonstrating robust performance in accurate classification. * **Boosting algorithms: XGBoost, gradient boosting,** and **CatBoost** showed strong performance with precision scores ranging from 0.90 to 0.91. **AdaBoost** performed slightly worse, achieving a precision score of around 0.89, which may require further tuning to the complexity of the dataset. * **Traditional and linear models:** models like **SVM, decision trees,** and **linear SVC** demonstrated reliable performance, achieving precision scores of around 0.92. * **Lower performers:** the **MLP classifier** and **Naive Bayes** achieved precision scores of around 0.89, suggesting limitations in handling the dataset's structure. Ensemble methods, especially **Bagging** and **LightGBM**, excelled in precision, making them suitable for clinical decision-making tasks. In contrast, the somewhat weaker performance of **K-nearest neighbors** and other models highlights the importance of proper model selection and parameter optimization. The recall scores for the machine learning models, depicted in Figure 8, provide insights into the ability of each model to identify true positive cases among all actual positives. High recall is essential in clinical settings to minimize false negatives, ensuring critical conditions are not overlooked. The results were as follows:  
* **Top performers: Bagging** and **LightGBM** achieved the highest recall score (0.91 to 0.93), reflecting superior sensitivity in detecting positive cases. * **Consistently high recall: random forest, logistic regression, SVM,** and **gradient boosting** showed strong recall scores (0.91 to 0.90), indicating reliable detection of positive cases across different classes. * **Moderate performance: K-nearest neighbors (KNN), AdaBoost,** and the **MLP classifier** achieved recall scores of around 0.87 to 0.89, respectively, suggesting moderate effectiveness in identifying true positives. \begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Parameters/Settings** \\ \hline Linear SVC & C = 1.0, penalty = \textasci{{}^{\prime}}12\textasci{{}^{\prime}},\textasci{{}^{\prime}}\textasci{{}  
The highest recall scores were achieved by ensemble models such as **LightGBM** and **Bagging** (and several other models), highlighting their suitability for applications requiring high sensitivity. The slightly lower performance of **KNN** and **Naive Bayes** suggests the need for further optimization or alternative strategies to improve their recall capability. These results highlight the importance of balancing recall with other metrics for comprehensive model evaluation. The F1-score, presented in Figure 9, combines precision and recall into a single metric, providing a balanced measure of a model's accuracy in both identifying true positives and  
Figure 8: Recall score results for the designed models. Figure 7: Precision score results for the designed models. avoiding false positives. It is particularly useful when dealing with imbalanced datasets. A summary of the results is as follows:  
* **Top performer: LightGBM** achieved the highest F1-score (around 0.93), demonstrating exceptional balance between precision and recall. * **Strong contenders:** models such as **random forest**, **logistic regression**, **SVM**, **gradient boosting**, and **CatBoost** all achieved high F1-scores in the range from 0.91 to 0.90.