## 4 Discussion  
The random forest outperformed the other algorithms, according to this study's findings. This classifier is a tree-structured base ensemble learner. Each tree predicts the target response independently, with the final estimates based on the average of the individual tree's estimates. It is a nonparametric technique that permits complicated interactions between predictor variables to be modeled automatically because there is no predetermined type of interaction between features and the result variable. This classifier is one of the most commonly utilized in the construction of CAD systems because of its benefits, which have been employed in a range of clinical situations. The most important features as determined by the RF classifier were examined since it proved to be the best among the other models. Figure 7 shows that the feature importances were calculated using the RF algorithm for classification purposes. C4H8O (2-Butanone) is the most important feature using the RF importance measure. Ketones can be derived from the amino acid metabolism. The rate of tissue protein catabolism is more or less constant under normal conditions. However, it has been identified that the amount of ketones is increased by protein metabolism in cachexia, which is associated with advanced cancer and other disease [34; 40]. Since cachexia usually occurs in the final stages of lung cancer, it leads to the formation of elevated ketones. Elevated concentrations of 2-butanone were found in the breath of lung cancer patients of both early stages and advanced stages by several international research groups [23; 27; 41]. Elevated 2-butanone was also found in cancer cell culture headspace samples [42]. These results lead us to hypothesize that, even if at the early stages, dysregulation of lung cancer cells and tissues causes oxidative stress and high protein catabolism in a local cancer environment, which results in elevated concentrations of 2-butanone in the exhaled breath of lung cancer patients. Moreover C4H8O (2-Butanone) has the highest Pearson correlation coefficient value and is ranked first using the mRMR technique. This explains that the value of this feature may be considered a good indicator for lung cancer diagnosis. Table 7 shows the results using only the values of the C4H8O (2-Butanone) feature. The accuracy results using SVM, LR, bagging and NN classifiers are 86%, which approaches the best result obtained using the RF classifier when using all the features (87%). This leads us to the conclusion that C4H8O (2-Butanone) can be used as a significant indication for the diagnosis of lung cancer. The accuracy results using the 17 features selected using the mRMR technique is also 87%, so we can drop the 10 features that decrease the complexity of training and still have the highest achieved accuracy using all the features (87%). Finally, all results show that this technique is practical for use in medical applications as it gives an accurate classification for the lung cancer without any need for an invasive and expensive method, such as biopsy or depending on multiple CT scans, which are needed to check the nodule's growth rate. \begin{table}
\begin{tabular}{l l l l l} \hline
**Algorithm** & **Accuracy** & **Sensitivity** & **Specificity** & **F-Score** \\ \hline SVM & 0.86 & 0.80 & 0.92 & 0.85 \\ \hline LR & 0.86 & 0.80 & 0.92 & 0.85 \\ \hline kNN & 0.77 & 0.68 & 0.86 & 0.75 \\ \hline NB & 0.81 & 0.66 & 0.96 & 0.78 \\ \hline DT & 0.65 & 0.58 & 0.73 & 0.62 \\ \hline RF & 0.66 & 0.60 & 0.73 & 0.64 \\ \hline Bagging & 0.86 & 0.80 & 0.92 & 0.85 \\ \hline AdaBoost & 0.66 & 0.60 & 0.73 & 0.64 \\ \hline NN & 0.86 & 0.80 & 0.92 & 0.85 \\ \hline \end{tabular}
\end{table} 
Table 7: Results using C4H8O (2-Butanone). **Author Contributions:** Conceptualization, A.E., A.S. ([PERSON]), A.S. ([PERSON]) and A.E.-B. ; breath analysis, X.-A.F. and M.H.N. ; methodology, A.E., A.S. ([PERSON]) and A.S. ([PERSON]); validation, A.S. ([PERSON]) and A.S. ([PERSON]); formal analysis, A.E., A.S. ([PERSON]), A.S. ([PERSON]) and A.E.-B.