## 3 Results  
The proposed lung cancer diagnosis system's performance is measured quantitatively using well-known metrics such as accuracy, sensitivity, specificity and F-score. These metrics are computed using true positive (TP), false positive (FP), true negative (TN) and false negative (FN), as clarified in the equations from Equations (8)-(12). TP is the number  
Figure 3: Simple neural network architecture. of patients with a malignant lung nodule that is correctly classified as malignant. FP is the number of patients with a benign lung nodule that is incorrectly classified as malignant. TN is the number of patients with a benign lung nodule that is correctly classified as benign. FN is the number of patients with a malignant lung nodule that is incorrectly classified as benign. \[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN}+ \text{FN}} \tag{8}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{9}\]  
\[\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{10}\]  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{11}\]  
\[\text{F}-\text{Score}=\frac{2\times\text{Precision}\times\text{Sensitivity}}{ \text{Precision}+\text{Sensitivity}} \tag{12}\]  
Tables 2-4 show the results with different holdout testing techniques. These different techniques are used to train our model by using different sizes of data to check the robustness of the model and to make sure that there is no overfitting to the data in any way. Table 2 uses 80% of the data as the training set and 20% as the testing set. Table 3 uses 75% of the data as the training set and 25% as the testing set, while Table 4 uses 70% of the data as the training set and 30% as the testing set. The best results are achieved using the 80-20% train-test split with random forest classifier (87%, 88%, 86% and 87% for accuracy, sensitivity, specificity and F-score, respectively). Moreover, RF outperformed the other models in the 75-25% train-test split, achieving 84% accuracy, 86% sensitivity, 83% specificity and 84% for the F-score. For the 70-30% train-test split, the SVM, RF and bagging classifiers achieved 82% accuracy, but RF continues to outperform them with an 83% F-score. From the results of these three tables, it can be concluded that the model is robust, and the features are distinguishing between malignant and benign nodules, regardless the technique used, as there is no big difference between the accuracy measures while using different amounts of data for training. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Algorithm** & **Accuracy** & **Sensitivity** & **Specificity** & **F-Score** \\ \hline SVM & 0.84 & 0.78 & 0.90 & 0.83 \\ \hline LR & 0.82 & 0.74 & 0.90 & 0.80 \\ \hline kNN & 0.80 & 0.70 & 0.90 & 0.78 \\ \hline NB & 0.59 & 0.32 & 0.86 & 0.44 \\ \hline DT & 0.68 & 0.58 & 0.78 & 0.64 \\ \hline RF & 0.87 & 0.88 & 0.86 & 0.87 \\ \hline Bagging & 0.84 & 0.82 & 0.86 & 0.84 \\ \hline AdaBoost & 0.75 & 0.74 & 0.76 & 0.75 \\ \hline NN & 0.69 & 0.66 & 0.73 & 0.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results with 80–20% train-test split. The correlation coefficient between each feature and the target diagnosis is calculated and ranked in ascending order. The C\({}_{9}\)H\({}_{16}\)O\({}_{2}\), C\({}_{5}\)H\({}_{8}\)O, C\({}_{3}\)H\({}_{4}\)O and C\({}_{3}\)H\({}_{6}\)O features have negative correlations with the target diagnosis, while the rest of the features have a positive correlation. C\({}_{9}\)H\({}_{16}\)O\({}_{2}\) has the largest negative correlation (\(-0.066104\)), while C\({}_{4}\)H\({}_{8}\)O has the largest positive correlation (\(0.465866\)). The absolute value of the negative correlation coefficient between the feature and target diagnosis is calculated so all of the values are positive, and, the larger the value, the stronger the correlation. Then, we picked the features whose correlations were greater than 0.1; this resulted in 10 features. These 10 features were selected to be used with classification, while the rest of the 17 features were dropped.