## 3 Results
This model also exhibited exceptionally high positive predictive value (PPV) and true positive rate (TPR), both at 97.50%, indicating excellent balance between positive and negative class predictions. MLP achieved its optimal performance with 100 features, reaching an accuracy and F1 score of 96.50%. Furthermore, the model's sensitivity (TPR) was 97.00%, while specificity (TNR) stood at 96.00%, underscoring its high classification efficacy. Below are the confusion matrices for the best models (Figure 9). Figure 8: Changes in the values of ACC, TPR, and TNR parameters depending on the number of features for Random Forest classifications. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Number of**} & \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**ACC**} & \multirow{2}{*}{**TPR**} & \multirow{2}{*}{**TNR**} & \multirow{2}{*}{**PPV**} & \multirow{2}{*}{**NPV**} & \multirow{2}{*}{**MCC**} & \multirow{2}{*}{**F1 Score**} \\ \cline{5-5} \cline{8-10} \multicolumn{1}{c}{**Features**} & & & & & & & & \\ \hline \multirow{5}{*}{267} & NB & 83.00 & 76.50 & 89.50 & 76.50 & 89.50 & 66.60 & 81.80 \\ \cline{2-10}  & MLP & 94.50 & 93.50 & 95.50 & 93.50 & 95.50 & 89.00 & 94.44 \\ \cline{2-10}  & HT & 82.75 & 76.50 & 89.00 & 76.50 & 89.00 & 66.00 & 81.59 \\ \cline{2-10}  & KNN & 93.25 & 95.00 & 91.50 & 95.00 & 91.50 & 86.60 & 93.37 \\ \cline{2-10}  & RF & 90.25 & 90.00 & 95.00 & 90.00 & 90.50 & 80.50 & 90.25 \\ \hline \multirow{5}{*}{200} & NB & 87.00 & 84.50 & 89.50 & 84.50 & 89.50 & 74.10 & 86.64 \\ \cline{2-10}  & MLP & 95.00 & 94.50 & 95.50 & 94.50 & 95.50 & 90.00 & 95.00 \\ \cline{2-10}  & HT & 86.50 & 84.00 & 89.00 & 84.00 & 89.00 & 73.10 & 86.14 \\ \cline{2-10}  & KNN & 94.25 & 96.50 & 92.00 & 96.50 & 96.50 & 88.60 & 94.35 \\ \cline{2-10}  & RF & 90.75 & 91.00 & 90.50 & 91.00 & 90.50 & 81.50 & 90.75 \\ \hline \multirow{5}{*}{150} & NB & 87.25 & 85.50 & 89.00 & 85.50 & 89.00 & 74.50 & 87.02 \\ \cline{2-10}  & MLP & 93.50 & 93.50 & 93.50 & 93.50 & 93.50 & 87.00 & 93.50 \\ \cline{2-10}  & HT & 87.00 & 85.50 & 88.50 & 85.50 & 88.50 & 74.00 & 86.78 \\ \cline{2-10}  & KNN & 94.75 & 96.50 & 93.00 & 96.50 & 93.00 & 89.60 & 94.82 \\ \cline{2-10}  & RF & 91.25 & 91.00 & 91.50 & 91.00 & 91.50 & 82.50 & 91.25 \\ \hline \multirow{5}{*}{100} & NB & 87.25 & 86.00 & 88.50 & 86.00 & 88.50 & 74.50 & 87.09 \\ \cline{2-10}  & MLP & 96.50 & 97.00 & 96.00 & 97.00 & 96.00 & 93.00 & 96.50 \\ \cline{2-10}  & HT & 87.25 & 86.00 & 88.50 & 86.00 & 88.50 & 74.50 & 87.09 \\ \cline{2-10}  & KNN & 96.50 & 98.50 & 94.50 & 98.50 & 94.50 & 93.10 & 96.56 \\ \cline{2-10}  & RF & 90.75 & 90.00 & 91.50 & 90.00 & 91.50 & 81.50 & 90.69 \\ \hline \multirow{5}{*}{50} & NB & 88.50 & 87.00 & 90.00 & 87.00 & 90.00 & 77.00 & 88.33 \\ \cline{2-10}  & MLP & 94.25 & 95.50 & 93.00 & 95.50 & 93.00 & 88.50 & 94.34 \\ \cline{2-10}  & HT & 88.75 & 87.00 & 90.50 & 87.00 & 90.50 & 77.50 & 88.57 \\ \cline{2-10}  & KNN & 96.75 & 97.50 & 96.00 & 97.50 & 96.00 & 93.50 & 96.79 \\ \cline{2-10}  & RF & 91.75 & 92.00 & 91.50 & 92.00 & 91.50 & 83.50 & 91.75 \\ \hline \multirow{5}{*}{20} & NB & 88.25 & 86.50 & 90.00 & 86.50 & 90.00 & 76.50 & 88.02 \\ \cline{2-10}  & NB & 88.25 & 86.50 & 90.00 & 85.50 & 90.00 & 75.60 & 87.45 \\ \cline{2-10}  & HT & 88.25 & 86.50 & 90.00 & 86.50 & 90.00 & 76.50 & 88.02 \\ \cline{2-10}  & KNN & 81.50 & 82.50 & 80.50 & 82.50 & 80.50 & 63.00 & 81.69 \\ \cline{2-10}  & RF & 87.00 & 86.00 & 88.00 & 86.00 & 88.00 & 74.00 & 86.89 \\ \hline \multirow{5}{*}{10} & NB & 88.25 & 86.50 & 90.00 & 86.50 & 90.00 & 76.50 & 88.02 \\ \cline{2-10}  & MLP & 88.00 & 85.00 & 90.50 & 85.50 & 90.50 & 76.10 & 87.69 \\ \cline{2-10}  & HT & 88.25 & 86.50 & 90.00 & 86.50 & 90.00 & 76.50 & 88.02 \\ \cline{2-10}  & KNN & 85.25 & 85.50 & 85.00 & 85.50 & 85.00 & 70.50 & 85.30 \\ \clineAmong the weakest classifiers were Hoeffding Tree and Naive Bayes. HT demonstrated consistently low metric values across most scenarios, particularly with 267 features, where its accuracy was 82.75% and F1 score was only 81.59%.