# Stroke Dataset Modeling: Comparative Study of Machine Learning Classification Methods
## 3 Results  
We compare methodologies A, B, and C by the number of off-diagonal elements of confusion matrices. For this purpose, we use the elements from the respective articles. For Methodology A, we present Table 2 for RF, DT, and SVM, results taken from [3]. We performed an additional experiment with the XGBoost model, as this model shows good performance against Methodologies B and C.  
Methodology A applied to the XGBoost model with parameters max_depth = 8 and random_state = 191 yielded results that we comment on in this section. The elements of the confusion matrix obtained by XGBoost are described in Table 2 as well. According to Table 2, SVM is the best at minimizing both false positives and false negatives, making it the most balanced model in terms of classification errors. RF also performs well with a low false negative rate but has a slightly higher false positive rate. XGBoost has a moderate error rate with a higher false positive tendency. DT, while perfect in avoiding false negatives, has the highest false positive rate, which may lead to more false alarms. In both methodologies, we analyzed three models from Tables 2 and 3, where the percentage of predicted observations is less than 3%. These models include SVM and RF, with error rates of 2.17% and 2.69%, respectively, from Table 2, and DSE, with an error rate of 2.81%, from Table 3. These models are anticipated to exhibit values for parameters like Accuracy, Precision, Sensitivity (Recall), and Specificity. The first article emphasizes the performance of the Support Vector Machine, which excels at minimizing positives and false negatives, achieving a top accuracy rate of 97.82% and a strong ROC score of 0.9975 (Tables 2 and 4). This illustrates that SVM accurately categorizes instances and excels in distinguishing between classes. Similarly, Random Forest demonstrates performance with an accuracy of 97.31% and the highest ROC score of 0.9994, indicating its ability to differentiate between positive and negative classes. XGBoost performs well with an accuracy of 96.47% and a ROC score of 0.9946. The Decision Tree has a flaw in that it excels at avoiding negatives but struggles with a false positive rate, lower accuracy (94.94%), and lower ROC score (0.9608). This suggests that it may need to be more dependable when managing classification errors. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Models** & **TN** & **FP** & **FN** & **TP** & **Total** & **(FN + FP)/Total (\%)** \\ \hline RF & 1373 & 56 & 2 & 726 & 2157 & 2.69 \\ \hline DT & 1283 & 109 & 0 & 765 & 2157 & 5.05 \\ \hline SVM & 1352 & 38 & 10 & 807 & 2207 & 2.17 \\ \hline XGBoost & 1326 & 73 & 3 & 755 & 2157 & 3.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The entries of confusion matrices were obtained by [PERSON] et al. [3]. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Models** & **TN** & **FP** & **FN** & **TP** & **Total** & **(FN + FP)/Total (\%)** \\ \hline XGBoost (Figure 12, [4]) & 1410 & 49 & 57 & 1401 & 2912 & 3.63 \\ \hline XGBoost (Figure 17, [4]) & 1362 & 40 & 47 & 1371 & 2820 & 3.09 \\ \hline DSE [4] & 1461 & 45 & 37 & 1374 & 2917 & 2.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The entries of confusion matrices were obtained by [PERSON] et al. [4]. The study by [PERSON] et al. [4] evaluated model performance using confusion matrices presented in Figures 12 and 17 from [4], summarized in Table 4. Table 5, derived from the confusion matrix data in Table 3, provides the calculated accuracy metrics for each model, as accuracy values were not directly reported in the original article. XGBoost (Figure 12 [4]), which was applied to imputed datasets, shows an accuracy of 96.36%. This was calculated from the confusion matrix values: 1410 true negatives (TN), 49 false positives (FP), 1401 true positives (TP), and 57 false negatives (FN). The combined error rate of 3.63% reflects a moderate level of misclassification, indicating that the model has some difficulty in correctly classifying all instances.