# Machine-Learning Applications in Oral Cancer: A Systematic Review
## 4 Discussion
According to the most frequent types of data per area of application, we found: (i) spectral data with 40.90% in the diagnosis and prevention area, (ii) medical/clinical data with 50.00% in prognosis, (iii) image data with 72.72% in potentially malignant oral lesions (pre-cancer), and (iv) genomic data representing 66.66% in the therapy and quality of life application area. As mentioned before, deep-learning has been applied in diverse areas (Table 1). In specific, it was used for the analysis of image and spectral data (Table 1) [20; 21; 24; 26; 28; 29; 34; 36; 38; 39; 40; 41; 42; 43; 44; 45; 52; 58; 60; 61; 62; 63; 65; 66; 67; 69]. The main areas of application of deep-learning were in diagnosis and prevention, and potentially malignant oral lesions (pre-cancer). In both areas, the images were the most used type of data, representing 83.33% and 100% of the total studies for diagnosis and prevention, and potentially malignant oral lesions (pre-cancer), respectively. The use of new technologies in high-quality image acquisition devices has made it possible to develop and enhance deep-learning approaches by employing convolutional neural networks. SVM is among the most used algorithms in ML by area of application. SVM is the focus of the largest number of studies, representing 45.45% and 63.63% of the total studies for diagnosis and prevention, and potentially malignant oral lesions (precancer), respectively. Furthermore, for prognosis, LR was the most frequently used algorithm, with 43.75%, while in therapy and quality of life, the algorithms CTREE, RF, BA, SVM, ANN, and DFG represented 100%. On the other hand, in the deep-learning subarea, the CNN stood out in diagnosis and prevention, and in potentially malignant oral lesions (precancer) with 100%. Studies varied according to the algorithm applied, the input and output variables, and the methods for assessing predictive model performance. SVM, ANN, and LR were the most commonly applied algorithms. Genomic, histopathological, image, medical/clinical, spectral, and speech data were the most often used to predict the four areas of application found in this review. ML, with algorithms such as SVM, ANN, LR, CNN, represents a powerful method, capable of effectively predicting outcomes in order to support diagnosis and prevention, prognosis, potentially malignant oral lesions (pre-cancer), and therapy and quality of life. It is important to note that not all these algorithms are intuitive. For example, ANN and SVM are nonlinear and inscrutable in the way they generate their outputs. In this sense, clinicians tend to lack trust in the outputs of a clinical decision support system when it is not clear how the algorithm gives the classification result, unlike decision trees, which identify the set of rules that there are behind the classification. In this aspect, it is very important to promote the transparency of these methods, which can be useful to facilitate their implementation in the field. This transparency can be achieved by reporting, in the case of SVM, the values of sigma (available only in some kernel types), C parameter, and type of kernel, whereas for ANN, it is important to report the number of layers and the corresponding number of nodes or neurons. Of the total included articles, most of the studies showed a low risk of bias. In addition, most of the studies used some type of validation. The most commonly applied validation method corresponded to a 5-fold CV and a 10-fold CV. In this aspect, avoiding the overfitting was a task considered in most of the studies included. Nevertheless, not all of the performance metrics were reported in all of the studies. Future studies would do well to report at least AUC, ACC, SE, and SP due to their importance in the analysis of the machine-learning method in order to enable the comparison between studies and facilitate performance evaluation.