## 4 Results
### Comparison of Machine Learning Algorithms' Performance
#### 4.2.1 Performance Comparison of All Algorithms on Sensor Data
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ventilation Training** & & & & & & \\
**Models** & & & & & & \\ \hline KNNs & 0.894 & 0.895 & 0.894 & 758.95 & 0.063 & 11.820 \\ \hline SVM & 0.659 & 0.665 & 0.668 & 452.18 & 6.990 & 3.238 \\ \hline Random Forest & 0.899 & 0.901 & 0.899 & 10,182.2 & 0.281 & 18.160 \\ \hline Decision Trees & 0.895 & 0.897 & 0.897 & 737.32 & 0.005 & 10.730 \\ \hline XGBoost & 0.723 & 0.727 & 0.721 & 30.625 & 0.068 & 70.602 \\ \hline Naive Bayes & 0.424 & 0.447 & 0.449 & 36.778 & 0.005 & 5.902 \\ \hline Logistic Regression & 0.560 & 0.569 & 0.575 & 79.824 & 0.156 & 3.160 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of performance metrics of ventilation training algorithms. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Crowd Training** & & & & & \\
**Models** & & & & & \\ \hline KNNs & 0.402 & 0.402 & 0.411 & 94.980 & 0.015 & 5.629 \\ \hline SVM & 0.268 & 0.321 & 0.326 & 725.271 & 11.595 & 5.352 \\ \hline Random Forest & 0.406 & 0.412 & 0.422 & 1143.8 & 0.187 & 9.668 \\ \hline Decision Trees & 0.396 & 0.412 & 0.435 & 141.59 & 0.005 & 6.629 \\ \hline XGBoost & 0.388 & 0.420 & 0.400 & 279.93 & 0.111 & 86.316 \\ \hline Naive Bayes & 0.243 & 0.233 & 0.316 & 53.552 & 0.013 & 2.004 \\ \hline Logistic Regression & 0.276 & 0.256 & 0.311 & 69.552 & 0.214 & 5.297 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of performance metrics of crowd training algorithms. 2.2 User General Evaluation Findings Obtained from All Sensor Data Using the K-Nearest Neighbors (KNNs) Algorithm  
Figure 7 shows the KNNs algorithm's ROC curve, derived from all sensor data, visualizing the sensitivity/specificity balance. The AUC value reflects the model's class discrimination ability and overall classification performance, evaluating its effectiveness. Figure 8 shows the KNNs algorithm's confusion matrix, derived from all sensor data. It visualizes the distribution of correct and incorrect classifications, indicating the model's strengths and weaknesses across classes. Figure 8: Visualization of the confusion matrix obtained with all sensor data for the KNNs algorithm. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**General Evaluation** & \multirow{2}{*}{**F1 Score \(\uparrow\)**} & \multirow{2}{*}{**Precision \(\uparrow\)**} & \multirow{2}{*}{**Recall \(\uparrow\)**} & \multirow{2}{*}{**Training Time (s) \(\downarrow\)**} & \multirow{2}{*}{**Test Time (s) \(\downarrow\)**} & \multirow{2}{*}{**Memory Usage (MB) \(\downarrow\)**} \\ \hline KNNs & 0.9904 & 0.9906 & 0.9901 & 1360.9 & 0.071 & 12.332 \\ \hline SVM & 0.945 & 0.951 & 0.941 & 3146.9 & 6.510 & 2.902 \\ \hline Random Forest & 0.9902 & 0.9905 & 0.9906 & 7309.2 & 0.422 & 21.762 \\ \hline Decision Trees & 0.984 & 0.984 & 0.984 & 330.470 & 0.005 & 8.785 \\ \hline XGBoost & 0.741 & 0.750 & 0.743 & 20.602 & 0.152 & 71.059 \\ \hline Naive Bayes & 0.429 & 0.434 & 0.450 & 36.160 & 0.004 & 5.992 \\ \hline Logistic Regression & 0.493 & 0.491 & 0.515 & 300.32 & 0.155 & 1.215 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of performance metrics of general evaluation training algorithms. Figure 7: ROC curve performance obtained with all sensor data for the KNN algorithm.