## 3 Results
### Interpretability  
In the fourth experiment, we built a decision tree to improve the interpretability using the variables with greater predictive power according to the Boruta method. The main advantage of decision trees is that they clearly show the procedure to achieve the classification results. Figure 5 shows a decision tree to classify individuals according to a set of attributes. A decision tree can display visually the rules to classify the subjects, which are very intuitive. Ideally, the decision tree will have a low number of attributes (trying not to compromise the performance of the model), so that users can understand the logic behind the model. There are four characteristics used in the model: Bilateral cataract, alcohol-intake, CFH2, and visual acuity in the left eye. The leaves of the tree (final nodes) represent the group to which each individual belongs. The green colour represents the group \"0\", that is, those subjects who do not suffer from the disease. On the other hand, to classify the sick individuals (group \"1\") the blue colour is used. Each node has three different rows, the number in the first row indicates the group, the two numbers in the second row indicate the probability of a successful or unsuccessful individual classification (the higher the colour intensity of the node, the greater the probability of correct classification). Finally, the number in the third row represents the proportion of individuals covered by the node. The tree is interpreted as follows: The first node corresponds to the bilateral-cataracts variable, which was found to be the most important variable according to our previous analysis. Then, if the attribute called bilateral-cataracts is equal to 1, the algorithm moves to the left; otherwise, it goes to the right.  
In the first node, the left 0.54 represents that 54% of the subjects do not have AMD, and the right 0.46 represents that 46% do have AMD. The instances to which this condition applies have a 0.71 probability of belonging to the group \"0\" (healthy subject) and 0.29 to group \"1\" (sick subject).  
Figure 4: The ROC curve representing the true positive rate versus false positive rate for each model.  
The first division corresponds to the alcohol-intake feature. If the variable is \"1\" as is written at the top of the node, 37% of the subjects are not sick. When the alcohol-intake is \"0\", 63% of the subjects suffer from the disease. The second split corresponds to the CH2 feature, if its value is \"0\", then 33% of the subjects do not have AMD and 67% of them suffer the disease. Next, the division is based on the variable visual acuity left eye, if it is greater than or equal to 29, then 71% subjects suffer from AMD. Otherwise, when the variable is less than 29, and if it is also less than 65, then 48% of the subjects are healthy and 52% have AMD.  
An important finding is that although the subject does not suffer Bilateral-cataract, alcohol-intake and CFH2 are risk factors for the disease. The decision tree shows that most of the subjects suffering from AMD do not have bilateral cataracts. This suggests that it is likely that bilateral cataracts will not be a risk factor for the disease. Nevertheless, further experimentation is needed to conclude that the association does not exist given that the dataset is small. While it is not a risk factor, it is still a relevant feature for diagnosis support.