## 3 Results
### Classification  
In the third experiment we compared the performance of ten ML classification algorithms to select the one that makes the most appropriate classification.  
In general, it is accepted that a probability greater than 0.5 is an adequate value to predict whether the example is part of a class, and if not, it is part of another class. The resulting values for all methods are presented in Table 2. The best value for the ROC metric was 83.51% with RF and for the sensitivity was 72.90% with K-nearest neighbors. The model with the best performance was K-nearest neighbors which outperforms the other models considering the four metrics.  
These results of classification are acceptable given that our dataset has only 256 examples. Learning from a few samples is still one of the most important machine learning challenges, given that ML methods sometimes require tens, hundreds, or thousands of training examples [27]. Hence, similar results were obtained with other classifiers. In our results, sensitivity presents a relatively high value, especially for the ML methods shrinkage discriminant analysis, neural networks with feature extraction, and random forest. This means that there are a small number of false negatives, which represents that the persons suffering from the disease were correctly classified.  
Additionally, Figure 4 shows the graph corresponding to the ROC metric for the cases in which the class variable was correctly predicted. That is to say, the subject suffers from AMD. The ROC figure represents a scenario with acceptable sensitivity. It shows that the best models for the ROC metric  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **ROC** & **Acc** & **AvgAcc** & **Sens** & **Spec** & **Prec** & **F1** \\ \hline Random & & & & & & \\ Forest & **0.8351** & 0.7492 & 0.7498 & 0.7889 & 0.7108 & 0.7255 & 0.7559 \\ \hline Glmnet & 0.8188 & 0.7469 & 0.7458 & 0.7698 & 0.7218 & 0.7518 & 0.7607 \\ \hline Neural & & & & & & \\ Networks & & & & & & \\ with Feature & & & & & & \\ Extraction & 0.8185 & 0.7539 & 0.7546 & 0.7937 & 0.7154 & 0.7299 & 0.7605 \\ \hline Shrinkage & & & & & & \\ Discriminant & & & & & & \\ Analysis & 0.817 & 0.75 & 0.7556 & **0.8152** & 0.6961 & 0.6891 & 0.7469 \\ \hline Stochastic & & & & & & \\ Gradient & & & & & & \\ Boosting & 0.812 & 0.7375 & 0.7363 & 0.7496 & 0.7229 & **0.7650** & 0.7572 \\ \hline Bayesian & & & & & & \\ Generalized & & & & & & \\ Linear & & & & & & \\ Model & 0.8118 & 0.732 & 0.731 & 0.7575 & 0.7045 & 0.7343 & 0.7457 \\ \hline Penalized & & & & & & \\ Discriminant & & & & & & \\ Analysis & 0.8111 & 0.7289 & 0.7284 & 0.7592 & 0.6975 & 0.7226 & 0.7404 \\ \hline K-Nearest & & & & & & & \\ Neighbors & 0.8086 & **0.757** & **0.7562** & 0.7833 & **0.7290** & 0.7547 & **0.7687** \\ \hline Multivariate & & & & & & & \\ Adaptive & & & & & & & \\ Regression & & & & & & \\ Spline & 0.8056 & 0.7328 & 0.732 & 0.761 & 0.703 & 0.7299 & 0.7451 \\ \hline Neural & & & & & & & \\ Network & 0.7994 & 0.7258 & 0.7246 & 0.7493 & 0.7 & 0.7328 & 0.741 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation of constructed models.  
are random forest (0.8351), Glmnet (0.8188), neural networks (0.8185), shrinkage (0.812), stochastic gradient (0.8118), Bayesian generalized linear model (0.8111), penalized discriminant analysis (0.8086).