## 4 Discussion
Similar results have been obtained by other authors [5; 6; 20] in the model development phase and based on the exclusive metrics of the same Scheimpflug technology. In contrast, our study obtained higher performance values for model development (AUC: 0.95) and similar ones in the validation phase (AUC: 0.85), and was composed of metrics that are not exclusive to the same Scheimpflug technology. [PERSON] et al. [50] used a machine-learning algorithm based on decision trees to analyze 55 parameters deriving from anterior and posterior corneal measurements. They found that the most discriminant variables related to posterior surface asymmetry and thickness spatial distribution achieved 93.6% sensitivity and 97.2% specificity when discriminating between normal and forme fruste KC. However, some authors [51] considered that including eyes from patients who had already been diagnosed with KC in one eye was inappropriate because it biases the sample if we contemplate that the genetic determinants for KC appearing are already present in them. In our study, the performance measurements of the early detection model in AUC, specificity and sensitivity terms indicated very high performance with the training dataset, with all three values reaching around 0.95, and very few false positives and false negatives. These figures are significantly lower for the validation procedure, which dropped to about 0.85, but were well over 80% in all cases. These findings indicate good model-validated performance, but also suggest the presence of some overfitting. This is otherwise reasonable if we take into account the relatively few training cases (62 healthy individuals and 44 RETICS grade I patients), which were significantly lower than the training values. Other authors have proposed using a multivariate system based on combining two different technologies. [PERSON] et al. [9] combined two technologies to propose a model based on 54 variables and six discriminant functions, and reported 93% sensitivity and 92% specificity in the model development phase. It was validated in a later study with 92% sensitivity and 96% specificity [52]. Other studies have suggested combining several different technologies [53; 54; 55], but the authors defined a more advanced form of KC when they included patients with manifest inferior steepening [49; 56]. The only reference found in the scientific literature with a classification system that uses visual acuity as a parameter is that by [PERSON] et al. [3], who established a scoring system that relied on five parameters (age, quality of vision, uncorrected distance visual acuity, refraction difference, maximum keratometry difference). However, this score did not classify disease grades, but disease progression. Consequently, its aim was to determine if crosslinking treatment would be necessary or not. Our work presents and validates a probability model of the risk associated with suffering KC. Our research group is unaware of any previous study that combined demographic, optical, pachymetric and morpho-geometric variables successfully and in real-time to detect early KC. Finally regarding the clinical KC development phase, experts' criteria have converged to diagnose this disease as the degree of severity of progressing KC [24]. Although there are several classifications that characterize clinical KC, to the authors' knowledge, there is no disease severity classifier based on patients' visual limitation. Our classifier is based on an ordinary logistic regression model that combines 17 variables and presents an overall accuracy of 0.698; that is, our model correctly classifies almost 70% of patients. The performance indicators for ordinal logistic regression are fairly lower, particularly for the RETICS grade I, II and III patients, with balanced accuracies ranging between 0.623 and 0.766. Grade II patients present the worst accuracy, with nine in every 18 patients being wrongly classified as grade I.