# Machine-Learning Applications in Oral Cancer: A Systematic Review
## 3 Results
### Predictive Model Evaluation  
The performance of the methods was reported by the authors in terms of the accuracy, area under the ROC curve, sensitivity, and/or specificity. However, not all of these metrics were reported all the time. In total, 57.89% of the studies reported sensitivity, 63.15% of the studies reported accuracy, 59.64% of the studies reported specificity, and 38.59% of the studies reported AUC. For instance, the metrics of ACC, AUC, SE, and SP, for the most part, used algorithms among the studies (Figure 3) and are shown in Table 2. Specifically, SVM gave an ACC of 85.83%, AUC of 0.83, SE of 86.45%, and SP of 88.20%. ANN gave an ACC of 75.72%, AUC of 0.69, SE of 76.90%, and SP of 84.59%. In the case of LR, the media for ACC, AUC, SE, and SP was 75.47%, 0.7 76.53%, and 77.51%, respectively. In addition, ANOVA analysis was performed (Table 2). Statistical significance was obtained for metrics of ACC, SE, and SP (\(p\)-value \(<\) 0.05). According to the studies included in the analysis, 94.73% present some method of validation of the model, as shown in Table 3. Only four studies did not use a validation method (Table 3). Specifically, the \"hold out\" validation was used in 31.57 % of the studies. The hold out validation consists of a method that divides the dataset into training and test set. The other most used method corresponds to the \"cross validation\" (CV), with 50.87% of the studies. This validation method considers the division of the set-in k-folds, where it uses one of the subsets as test data and the rest (K-1) as training data. Finally, the most used validations among the different studies were 5 and 10-fold CV (Table 3). Figure 3: Distribution of publications per year according to different algorithms employed. In this graphic are plotted those algorithms that are most usually used. The performance of the top three most frequently applied algorithms (SVM, ANN, and LR) was compared statistically with each other. Algorithms were compared according \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Performance Metrics Mean (SD; \(n\))**} \\ \hline  & **Accuracy** & **Sensitivity** & **Specificity** & **AUC** \\  & **\%** & **\%** & **\%** & **\%** \\ \hline SVM & 85.83 (10.01; 19) & 86.45 (8.06; 17) & 88.20 (10.72; 15) & 0.83 (0.15; 9) \\ \hline ANN & 75.72 (13.08; 8) & 76.90 (13.65; 9) & 84.59 (13.44; 8) & 0.69 (0.14; 5) \\ \hline LR & 75.47 (12.67; 9) & 76.53 (13.68; 6) & 77.51 (10.78; 4) & 0.7 (0.14; 4) \\ \hline ANOVA & \(p\)-value = 0.037 & \(p\)-value = 0.058 & \(p\)-value = 0.270 & \(p\)-value = 0.213 \\ \hline  & \multicolumn{3}{c}{**Diagnosis and Prevention**} \\ \hline SVM & 90.22 (5.79; 8) & 87.19 (4.69; 7) & 89.99 (7.80;7) & 0.95 (0.04; 3) \\ \hline ANN & 84.03 (20.18; 2) & 84.51 (7.97; 5) & 83.28 (15.48; 5) & 0.6 (0.18; 2) \\ \hline LR & 93.50 (9.19; 2) & 87.00 (0; 1) & — & — \\ \hline ANOVA & \(p\)-value = 0.570 & \(p\)-value = 0.761 & \(p\)-value = 0.343 & \(p\)-value = 0.039 \\ \hline  & \multicolumn{3}{c}{**Prognosis**} \\ \hline SVM & 74.72 (13.19; 5) & 78.90 (11.95; 4) & 74.00 (26.87; 2) & 0.75 (0.16; 5) \\ \hline ANN & 71.55 (11.69; 5) & 61.17 (7.80; 3) & 80.15 (4.44; 2) & 0.76 (0.09; 3) \\ \hline LR & 68.21 (5.97; 6) & 73.05 (15.98; 4) & 74.35 (10.69; 3) & 0.7 (0.14; 4) \\ \hline ANOVA & \(p\)-value = 0.600 & \(p\)-value = 0.249 & \(p\)-value = 0.903 & \(p\)-value = 0.861 \\ \hline  & \multicolumn{3}{c}{**Potentially malignant oral lesions (pre-cancer)**} \\ \hline SVM & 89.69 (2.65; 5) & 90.61 (5.38; 6) & 90.86 (3.34; 6) & — \\ \hline ANN & — & 86.00 (0; 1) & 100 (0; 1) & — \\ \hline LR & 83.00 (0; 1) & 80.00 (0; 1) & 87.00 (0; 1) & — \\ \hline ANOVA & p-value = 0.082 & p-value = 0.255 & p-value = 0.083 & — \\ \hline  & \multicolumn{3}{c}{**Therapy and quality of life**} \\ \hline SVM & 87.00 (0; 1) & — & — & 0.89 (0; 1) \\ \hline ANN & 80.00 (0; 1) & — & — & — \\ \hline LR & — & — & — & — \\ \hline ANOVA & — & — & — & — \\ \hline \hline \end{tabular}
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